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Sökning: WFRF:(Strand Robin 1978 ) > (2020-2024)

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1.
  • Eriksson, Jan W, et al. (författare)
  • Tissue-specific glucose partitioning and fat content in prediabetes and type 2 diabetes: whole-body PET/MRI during hyperinsulinemia
  • 2021
  • Ingår i: European journal of endocrinology. - : Bioscientifica. - 0804-4643 .- 1479-683X. ; 184:6, s. 879-899
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective: To obtain direct quantifications of glucose turnover, volumes an d fat content of several tissues in the development of type 2 diabetes (T2D) using a novel integrated a pproach for whole-body imaging. Design and methods: Hyperinsulinemic-euglycemic clamps and simultaneous whole-body integrated [18F]FDG-PET/MRI with automated analyses were performed in control (n = 12), prediabetes (n = 16) and T2D (n = 13) subjects matched for age, sex and BMI. Results: Whole-body glucose uptake (Rd) was reduced by approximately 25% in T2D vs control subjects, and partitioning to brain was increased from 3.8% of total Rd in co ntrols to 7.1% in T2D. In liver, subcutaneous AT, thigh muscle, total tissue glucose metabolic rates (MRglu) and their % of total Rd were reduced in T2D compared to contr ol subjects. The prediabetes group had intermediate findings. Total MRglu in heart, visceral AT, gluteus and calf muscle was similar across groups. Whole-body insulin sensitivity asses sed as glucose infusion rate correlated with liver MR glu but inversely with brain MRglu. Liver fat content correlated with MRglu in brain but inversely with MRglu in other tissues. Calf muscle fat was inversely associated with MR glu only in the same muscle group. Conclusions: This integrated imaging approach provides detailed quantification of tissue-specific glucose metabolism. During T2D development, insulin-stimulated glucose disposal is impaired and increasingly shifted away from muscle, liver and fat toward the brain. Altered glucose handling in the brain and liver fat accumulation may aggravate insulin resistance in several organs. © 2021 BioScientifica Ltd.. All rights reserved.
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2.
  • Guglielmo, Priscilla, et al. (författare)
  • Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition
  • 2020
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [18F]FDG-PET/MR images. Twelve subjects underwent whole-body [18F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.
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3.
  • Ahmad, Nouman, et al. (författare)
  • Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
  • 2023
  • Ingår i: BMC BIOINFORMATICS. - : BioMed Central (BMC). - 1471-2105. ; 24:1
  • Tidskriftsartikel (refereegranskat)abstract
    • BackgroundBody composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.MethodsThe study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.ResultsThe Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.ConclusionFully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.
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4.
  • Ahmad, Nouman, et al. (författare)
  • Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol : methodology and proof-of-concept studies
  • 2024
  • Ingår i: Biomedical engineering online. - : Springer Nature. - 1475-925X. ; 23:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.Methods The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.Results Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.Conclusion The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.
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6.
  • Asplund, Teo, et al. (författare)
  • Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions
  • 2020
  • Ingår i: Mathematical Morphology : Theory and Applications. - : Walter de Gruyter. - 2353-3390. ; 4:1, s. 108-126
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper proposes a way of better approximating continuous, two-dimensional morphologyin the discrete domain, by allowing for irregularly sampled input and output signals. We generalizeprevious work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentallywe show improved results over regular, discrete morphology with respect to the approximation ofcontinuous morphology. It is also worth noting that the number of output samples can often be reducedwithout sacrificing the quality of the approximation, since the morphological operators usually generateoutput signals with many plateaus, which, intuitively do not need a large number of samples to be correctlyrepresented. Finally, the paper presents some results showing adaptive morphology on irregularlysampled signals.
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8.
  • Banerjee, Subhashis, et al. (författare)
  • Deep Curriculum Learning for Follow-up MRI Registration in Glioblastoma
  • 2023
  • Ingår i: Medical Imaging 2023. - : SPIE -Society of Photo-Optical Instrumentation Engineers. - 9781510660335 - 9781510660342
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a weakly supervised deep convolutional neural network-based approach to perform voxel-level3D registration between subsequent follow-up MRI scans of the same patient. To handle the large deformation inthe surrounding brain tissues due to the tumor’s mass effect we proposed curriculum learning-based training forthe network. Weak supervision helps the network to concentrate more focus on the tumor region and resectioncavity through a saliency detection network. Qualitative and quantitative experimental results show the proposedregistration network outperformed two popular state-of-the-art methods.
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9.
  • Banerjee, Subhashis, et al. (författare)
  • Lifelong Learning with Dynamic Convolutions for Glioma Segmentation from Multi-Modal MRI
  • 2023
  • Ingår i: Medical imaging 2023. - : SPIE - International Society for Optical Engineering. - 9781510660335 - 9781510660342
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a novel solution for catastrophic forgetting in lifelong learning (LL) using Dynamic Convolution Neural Network (Dy-CNN). The proposed dynamic convolution layer can adapt convolution filters by learning kernel coefficients or weights based on the input image. The suitability of the proposed Dy-CNN in a lifelong sequential learning-based scenario with multi-modal MR images is experimentally demonstrated for the segmentation of Glioma tumors from multi-modal MR images. Experimental results demonstrated the superiority of the Dy-CNN-based segmenting network in terms of learning through multi-modal MRI images and better convergence of lifelong learning-based training.
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10.
  • Banerjee, Subhashis, et al. (författare)
  • Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net
  • 2021
  • Ingår i: 25th International Conference on Pattern Recognition (ICPR). - 9781728188089 ; , s. 9265-9272
  • Konferensbidrag (refereegranskat)abstract
    • Due to the advancement of non-invasive medical imaging modalities like Magnetic Resonance Angiography (MRA), an increasing number of Intracranial Aneurysm (IA) cases are being reported in recent years. The IAs are typically treated by so-called endovascular coiling, where blood flow in the IA is prevented by embolization with a platinum coil. Accurate quantification of the IA Remnant (IAR), i.e. the volume with blood flow present post treatment is the utmost important factor in choosing the right treatment planning. This is typically done by manually segmenting the aneurysm remnant from the MRA volume. Since manual segmentation of volumetric images is a labour-intensive and error-prone process, development of an automatic volumetric segmentation method is required. Segmentation of small structures such as IA, that may largely vary in size, shape, and location is considered extremely difficult. Similar intensity distribution of IAs and surrounding blood vessels makes it more challenging and susceptible to false positive. In this paper we propose a novel 3D CNN architecture called Dual-Attention Atrous Net (DAtt-ANet), which can efficiently segment IAR volumes from MRA images by reconciling features at different scales using the proposed Parallel Atrous Unit (PAU) along with the use of self-attention mechanism for extracting fine-grained features and intra-class correlation. The proposed DAtt-ANet model is trained and evaluated on a clinical MRA image dataset of IAR consisting of 46 subjects. We compared the proposed DAtt-ANet with five state-of-the-art CNN models based on their segmentation performance. The proposed DAtt-ANet outperformed all other methods and was able to achieve a five-fold cross-validation DICE score of 0.73 +/- 0.06.
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11.
  • Banerjee, Subhashis, et al. (författare)
  • Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring
  • 2024
  • Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 14:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.
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12.
  • Banerjee, Subhashis, et al. (författare)
  • Topology-Aware Learning for Volumetric Cerebrovascular Segmentation
  • 2022
  • Ingår i: 2022 IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2022). - : IEEE. - 9781665429238 ; , s. 1-4
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
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13.
  • Bengtsson Bernander, Karl, et al. (författare)
  • Replacing data augmentation with rotation-equivariant CNNs in image-based classification of oral cancer
  • 2021
  • Ingår i: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. - Cham : Springer International Publishing. - 9783030934194 - 9783030934200 ; , s. 24-33
  • Konferensbidrag (refereegranskat)abstract
    • We present how replacing convolutional neural networks with a rotation-equivariant counterpart can be used to reduce the amount of training images needed for classification of whether a cell is cancerous or not. Our hypothesis is that data augmentation schemes by rotation can be replaced, thereby increasing weight sharing and reducing overfitting. The dataset at hand consists of single cell images. We have balanced a subset of almost 9.000 images from healthy patients and patients diagnosed with cancer. Results show that classification accuracy is improved and overfitting reduced if compared to an ordinary convolutional neural network. The results are encouraging and thereby an advancing step towards making screening of patients widely used for the application of oral cancer.
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14.
  • Breznik, Eva, et al. (författare)
  • Effects of distance transform choice in training with boundary loss
  • 2021
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Convolutional neural networks are the method of choice for many medical imaging tasks, in particular segmentation. Recently, efforts have been made to include distance measures in the network training, as for example the introduction of boundary loss, calculated via a signed distance transform. Using boundary loss for segmentation can alleviate issues with imbalance and irregular shapes, leading to a better segmentation boundary. It is originally based on the Euclidean distance transform. In this paper we investigate the effects of employing various definitions of distance when using the boundary loss for medical image segmentation. Our results show a promising behaviour in training with non-Euclidean distances, and suggest a possible new use of the boundary loss in segmentation problems.
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15.
  • Breznik, Eva (författare)
  • Image Processing and Analysis Methods for Biomedical Applications
  • 2023
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • With new technologies and developments medical images can be acquired more quickly and at a larger scale than ever before. However, increased amount of data induces an overhead in the human labour needed for its inspection and analysis. To support clinicians in decision making and enable swifter examinations, computerized methods can be utilized to automate the more time-consuming tasks. For such use, methods need be highly accurate, fast, reliable and interpretable. In this thesis we develop and improve methods for image segmentation, retrieval and statistical analysis, with applications in imaging-based diagnostic pipelines. Individual objects often need to first be extracted/segmented from the image before they can be analysed further. We propose methodological improvements for deep learning-based segmentation methods using distance maps, with the focus on fully-supervised 3D patch-based training and training on 2D slices under point supervision. We show that using a directly interpretable distance prior helps to improve segmentation accuracy and training stability.For histological data in particular, we propose and extensively evaluate a contrastive learning and bag of words-based pipeline for cross-modal image retrieval. The method is able to recover correct matches from the database across modalities and small transformations with improved accuracy compared to the competitors. In addition, we examine a number of methods for multiplicity correction on statistical analyses of correlation using medical images. Evaluation strategies are discussed and anatomy-observing extensions to the methods are developed as a way of directly decreasing the multiplicity issue in an interpretable manner, providing improvements in error control. The methods presented in this thesis were developed with clinical applications in mind and provide a strong base for further developments and future use in medical practice.
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  • Breznik, Eva, et al. (författare)
  • Multiple comparison correction methods for whole-body magnetic resonance imaging
  • 2020
  • Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 7:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses. Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions. Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate. Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.
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18.
  • Brunner, David, et al. (författare)
  • Efficient Parallel Thinning of 3d Objects on the Body-centered Cubic Lattice
  • 2022
  • Ingår i: Computer-Aided Design. - : Elsevier. - 0010-4485 .- 1879-2685. ; 151
  • Tidskriftsartikel (refereegranskat)abstract
    • We consider thinning methods to extract one dimensional skeletons from discrete objects defined on the body-centered cubic (bcc) lattice. In Strand (2004), a condition has been given that guarantees the preservation of the object's topology in such a thinning process. In this paper, we present stronger conditions that even allow the topological invariant point removal in a parallelized process. These conditions for p-simplicity can be efficiently evaluated which leads to a very fast thinning process. We show that p-simplicity is a new concept that cannot be obtained by adapting the checking plane conditions of Tsao and Fu to the bcc lattice. Furthermore, we introduce distance information and an optional pruning mechanism into the thinning process to improve the quality of the resulting skeletons. The presented results show that our method generates high quality skeletons that reproduce the symmetries of the models even under the condition of added noise and contain only very few spurious branches. The presented running times demonstrate the linear run-time behavior of our algorithm and the speedup that is achieved by the parallelization. (C) 2022 Elsevier Ltd. All rights reserved.
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19.
  • Ekström, Simon, 1991- (författare)
  • Efficient GPU-based Image Registration : for Detailed Large-Scale Whole-body Analysis
  • 2020
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Imaging has become an important aspect of medicine, enabling visualization of internals in a non-invasive manner. The rapid advancement and adoption of imaging techniques have led to a demand for tools able to take advantage of the information that is produced. Medical image analysis aims to extract relevant information from acquired images to aid diagnostics in healthcare and increase the understanding within medical research. The main subject of this thesis, image registration, is a widely used tool in image analysis that can be employed to find a spatial transformation aligning a set of images. One application, that is described in detail in this thesis, is the use of image registration for large-scale analysis of whole-body images through the utilization of the correspondences defined by the resulting transformations. To produce detailed results, the correspondences, i.e. transformations, need to be of high resolution and the quality of the result has a direct impact on the quality of the analysis. Also, this type of application aims to analyze large cohorts and the value of a registration method is not only weighted by its ability to produce an accurate result but also by its efficiency. This thesis presents two contributions on the subject; a new method for efficient image registration with the ability to produce dense deformable transformations, and the application of the presented method in large-scale analysis of a whole-body dataset acquired using an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system. In this thesis, it is shown that efficient and detailed image registration can be performed by employing graph cuts and a heuristic where the optimization is performed on subregions of the image. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging.
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20.
  • Ekström, Simon, 1991-, et al. (författare)
  • Fast graph-cut based optimization for practical dense deformable registration of volume images
  • 2020
  • Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier. - 0895-6111 .- 1879-0771. ; 84
  • Tidskriftsartikel (refereegranskat)abstract
    • Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas-based image segmentation. Registration is often phrased as an optimization problem, i.e., finding a deformation field that is optimal according to a given objective function. Discrete, combinatorial, optimization techniques have successfully been employed to solve the resulting optimization problem. Specifically, optimization based on α-expansion with minimal graph cuts has been proposed as a powerful tool for image registration. The high computational cost of the graph-cut based optimization approach, however, limits the utility of this approach for registration of large volume images. Here, we propose to accelerate graph-cut based deformable registration by dividing the image into overlapping sub-regions and restricting the α-expansion moves to a single sub-region at a time. We demonstrate empirically that this approach can achieve a large reduction in computation time - from days to minutes - with only a small penalty in terms of solution quality. The reduction in computation time provided by the proposed method makes graph-cut based deformable registration viable for large volume images. Graph-cut based image registration has previously been shown to produce excellent results, but the high computational cost has hindered the adoption of the method for registration of large medical volume images. Our proposed method lifts this restriction, requiring only a small fraction of the computational cost to produce results of comparable quality.
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21.
  • Ekström, Simon, 1991-, et al. (författare)
  • Faster dense deformable image registration by utilizing both CPU and GPU
  • 2021
  • Ingår i: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 8:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
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23.
  • Fransson, Samuel, et al. (författare)
  • Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy
  • 2021
  • Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 20, s. 17-22
  • Tidskriftsartikel (refereegranskat)abstract
    • Background and purpose: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.Materials and methods: 4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.Results: The registration algorithm produced accurate registration result, in general DICE overlap >0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm.onclusions: An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.
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24.
  • Fransson, Samuel, 1991- (författare)
  • Machine Learning in Magnetic Resonance-Guided Adaptive Radiotherapy
  • 2024
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • In radiotherapy, treatments are frequently distributed over multiple weeks, and the radiation dose delivered across several sessions. A significant hurdle in this approach is the anatomical changes that occur between the planning stage and subsequent treatment sessions, leading to uncertainties in the treatment. The MR-Linac system, which combines a linear accelerator with an MRI scanner, addresses this issue by allowing for daily adjustments to the treatment plan based on the patient's current anatomy. However, the process for making these adjustments, involving image fusion, re-contouring, and plan re-optimization, can be quite elaborate and time-consuming. This project aimed to identify opportunities within the daily treatment routine where machine learning and deep learning could streamline the process, thereby enhancing efficiency, with a focus on prostate cancer treatments due to their frequent occurrence at our facility. We leveraged deep learning to train patient-specific models for segmenting anatomical structures in daily MRI scans, matching the accuracy of existing deformable image registration techniques. Furthermore, we extended this concept to segmenting structures and predicting radiation dose distributions, offering a swift assessment of potential dose distribution before engaging in the more complex manual workflow. This could aid in selecting the most suitable adaptation method more quickly. Additionally, we developed motion models for intrafractional motion and for segmenting images at lower resolutions to facilitate a target tracking process. Throughout this project, we showed how machine learning and deep learning techniques could contribute to optimizing the daily MR-Linac workflow.
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25.
  • Fransson, Samuel, et al. (författare)
  • Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy
  • 2022
  • Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 23, s. 38-42
  • Tidskriftsartikel (refereegranskat)abstract
    • Background and Purpose: Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training. Mitigating the problem of scarcity of data, we propose patient specific networks trained on a single dataset for each patient, for contouring onto the following datasets in an adaptive MR-Linacworkflow. Materials and Methods: MR-scans from 17 prostate patients treated on an MR-Linac with contours of Clinical Target Volume (CTV), bladder and rectum were utilized. U-net shaped models were trained based on the image from the first fraction of each patient, and subsequently applied onto the following treatment images. Results were compared with manual contours in terms of the Dice coefficient and Added Path Length (APL). As benchmark, contours propagated through the clinical DIR algorithm were similarly evaluated. Results: In Dice coefficient the ANN output was 0.92 +/- 0.03, 0.93 +/- 0.07 and 0.84 +/- 0.10 while for DIR 0.95 +/- 0.03, 0.93 +/- 0.08, 0.88 +/- 0.06 for CTV, bladder and rectum respectively. Similarly, APL where 3109 +/- 1642, 7250 +/- 4234 and 5041 +/- 2666 for ANN and 1835 +/- 1621, 7236 +/- 4287 and 4170 +/- 2920 voxels for DIR. Conclusions: Patient specific ANN models trained on images from the first fraction of a prostate MR-Linac treatment showed similar accuracy when applied to the subsequent fraction images as a clinically implemented DIR method.
  •  
26.
  • Jansen, Marielle J A, et al. (författare)
  • Patient-specific fine-tuning of CNNs for follow-up lesion quantification
  • 2020
  • Ingår i: Journal of Medical Imaging.
  • Tidskriftsartikel (refereegranskat)abstract
    • Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNNbased methods have the potential to extract valuable information from previously acquired imaging to better quantify current imaging of the same patient. A pre-trained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning. In this work, we studied the improvement in performance of lesion quantification methods on MR images after fine-tuning compared to a base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. In this study we showed that patient-specific fine-tuning has potential to improve the lesion quantification performance of general CNNs by exploiting the patient’s previously acquired imaging
  •  
27.
  • Jansen, Marielle J. A., et al. (författare)
  • Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification
  • 2020
  • Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 7:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient.Approach: A pretrained CNN can be updated with a patient's previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH).Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87.Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient's previously acquired imaging.
  •  
28.
  • Jönsson, Hanna, et al. (författare)
  • An image registration method for voxel-wise analysis of whole-body oncological PET-CT
  • 2022
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient's disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted towards computer-aided voxel-wise analysis of whole-body PET-CT data was developed. The method used both CT images and tissue segmentation masks in parallel to spatially align images step-by-step. To evaluate its performance, a set of baseline PET-CT images of 131 classical Hodgkin lymphoma (cHL) patients and longitudinal image series of 135 head and neck cancer (HNC) patients were registered between and within subjects according to the proposed method. Results showed that major organs and anatomical structures generally were registered correctly. Whole-body inverse consistency vector and intensity magnitude errors were on average less than 5 mm and 45 Hounsfield units respectively in both registration tasks. Image registration was feasible in time and the nearly automatic pipeline enabled efficient image processing. Metabolic tumor volumes of the cHL patients and registration-derived therapy-related tissue volume change of the HNC patients mapped to template spaces confirmed proof-of-concept. In conclusion, the method established a robust point-correspondence and enabled quantitative visualization of group-wise image features on voxel level.
  •  
29.
  • Kundu, Swagata, et al. (författare)
  • 3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation
  • 2023
  • Ingår i: Pattern Recognition and Machine Intelligence, PREMI 2023. - : Springer. - 9783031451690 - 9783031451706 ; , s. 380-387
  • Konferensbidrag (refereegranskat)abstract
    • Accurate localization and volumetric quantification of postoperative glioblastoma are of profound importance for clinical applications like post-surgery treatment planning, monitoring of tumor regrowth, and radiotherapy map planning. Manual delineation consumes more time and error prone thus automated 3-D quantification of brain tumors using deep learning algorithms from MRI scans has been used in recent years. The shortcoming with automated segmentation is that it often over-segments or under-segments the tumor regions. An interactive deep-learning tool will enable radiologists to correct the over-segmented and under-segmented voxels. In this paper, we proposed a network named Attention-SEV-Net which outperforms state-of-the-art network architectures. We also developed an interactive graphical user interface, where the initial 3-D segmentation of contrast-enhanced tumor can be interactively corrected to remove falsely detected isolated tumor regions. Attention-SEV-Net is trained with BraTS-2021 training data set and tested on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like U-Net, VNet, Attention U-Net and Residual U-Net. The mean dice score achieved is 0.6682 and the mean Hausdorff distance-95 got is 8.96mm for the Uppsala University dataset.
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30.
  • Kundu, Swagata, et al. (författare)
  • ASE-Net for Segmentation of Post-operative Glioblastoma and Patient-specific Fine-tuning for Segmentation Refinement of Follow-up MRI Scans
  • 2024
  • Ingår i: SN computer science. - : Springer. - 2661-8907. ; 5:106
  • Tidskriftsartikel (refereegranskat)abstract
    • Volumetric quantification of tumors is usually done manually by radiologists requiring precious medical time and suffering from inter-observer variability. An automatic tool for accurate volume quantification of post-operative glioblastoma would reduce the workload of radiologists and improve the quality of follow-up monitoring and patient care. This paper deals with the 3-D segmentation of post-operative glioblastoma using channel squeeze and excitation based attention gated network (ASE-Net). The proposed deep neural network has a 3-D encoder and decoder based architecture with channel squeeze and excitation (CSE) blocks and attention blocks. The CSE block reduces the dependency on space information and put more emphasize on the channel information. The attention block suppresses the feature maps of irrelevant background and helps highlighting the relevant feature maps. The Uppsala university data set used has post-operative follow-up MRI scans for fifteen patients. A patient specific fine-tuning approach is used to improve the segmentation results for each patient. ASE-Net is also cross-validated with BraTS-2021 data set. The mean dice score of five-fold cross validation results with BraTS-2021 data set for enhanced tumor is 0.8244. The proposed network outperforms the competing networks like U-Net, Attention U-Net and Res U-Net. On the Uppsala University glioblastoma data set, the mean Dice score obtained with the proposed network is 0.7084, Hausdorff Distance-95 is 7.14 and the mean volumetric similarity achieved is 0.8579. With fine-tuning the pre-trained network, the mean dice score improved to 0.7368, Hausdorff Distance-95 decreased to 6.10 and volumetric similarity improved to 0.8736. ASE-Net outperforms the competing networks and can be used for volumetric quantification of post-operative glioblastoma from follow-up MRI scans. The network significantly reduces the probability of over segmentation.
  •  
31.
  • Langner, Taro, et al. (författare)
  • Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
  • 2020
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.
  •  
32.
  • Langner, Taro, et al. (författare)
  • Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
  • 2020
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
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33.
  •  
34.
  • Langner, Taro, et al. (författare)
  • Large-scale Inference of Liver Fat with Neural Networks on UK Biobank Body MRI
  • 2020
  • Ingår i: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020. - Cham : Springer. - 9783030597122 - 9783030597139 ; , s. 602-611
  • Konferensbidrag (refereegranskat)abstract
    • The UK Biobank Imaging Study has acquired medical scans of more than 40,000 volunteer participants. The resulting wealth of anatomical information has been made available for research, together with extensive metadata including measurements of liver fat. These values play an important role in metabolic disease, but are only available for a minority of imaged subjects as their collection requires the careful work of image analysts on dedicated liver MRI. Another UK Biobank protocol is neck-to-knee body MRI for analysis of body composition. The resulting volumes can also quantify fat fractions, even though they were reconstructed with a two- instead of a three-point Dixon technique. In this work, a novel framework for automated inference of liver fat from UK Biobank neck-to-knee body MRI is proposed. A ResNet50 was trained for regression on two-dimensional slices from these scans and the reference values as target, without any need for ground truth segmentations. Once trained, it performs fast, objective, and fully automated predictions that require no manual intervention. On the given data, it closely emulates the reference method, reaching a level of agreement comparable to different gold standard techniques. The network learned to rectify non-linearities in the fat fraction values and identified several outliers in the reference. It outperformed a multi-atlas segmentation baseline and inferred new estimates for all imaged subjects lacking reference values, expanding the total number of liver fat measurements by factor six.
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35.
  • Langner, Taro, et al. (författare)
  • MIMIR : Deep Regression for Automated Analysis of UK Biobank MRI Scans
  • 2022
  • Ingår i: Radiology: Artificial Intelligence. - : Radiological Society of North America (RSNA). - 2638-6100. ; 4:3
  • Tidskriftsartikel (refereegranskat)abstract
    • UK Biobank (UKB) has recruited more than 500000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. (C) RSNA, 2022.
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36.
  • Langner, Taro, et al. (författare)
  • Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
  • 2021
  • Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier BV. - 0895-6111 .- 1879-0771. ; 93
  • Tidskriftsartikel (refereegranskat)abstract
    • Along with rich health-related metadata, an ongoing imaging study has acquired MRI of over 40,000 male and female UK Biobank participants aged 44-82 since 2014. Phenotypes derived from these images, such as measurements of body composition, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this retrospective study, six measurements of body composition were automatically estimated by ResNet50 neural networks for image-based regression from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine mean-variance regression and ensembling for predictive uncertainty estimation, which can quantify individual measurement errors and thereby help to identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8,500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1,000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years. 
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37.
  • Lind, Lars, et al. (författare)
  • Cardiovascular-related proteins and the abdominal visceral to subcutaneous adipose tissue ratio
  • 2021
  • Ingår i: NMCD. Nutrition Metabolism and Cardiovascular Diseases. - : Elsevier. - 0939-4753 .- 1590-3729. ; 31:2, s. 532-539
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND AND AIMS: An increased amount of visceral adipose tissues has been related to atherosclerosis and future cardiovascular events. The present study aims to investigate how the abdominal fat distribution links to plasma levels of cardiovascular-related proteins.METHOD AND RESULTS: In the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (n = 326, all aged 50 years), abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue volumes were quantified by MRI. Eighty-six cardiovascular-related proteins were measured by the proximity extension assay (PEA). Similar investigations were carried out in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (n = 400, all aged 75 years). In the discovery dataset (POEM), 10 proteins were related to the VAT/SAT-ratio using false discovery rate <.05. Of those, Cathepsin D (CTSD), Interleukin-1 receptor antagonist protein (IL-1RA) and Growth hormone (GH) (inversely) were related to the VAT/SAT-ratio in the validation in PIVUS following adjustment for sex, BMI, smoking, education level and exercise habits (p < 0.05). In a secondary analysis, a meta-analysis of the two samples suggested that 15 proteins could be linked to the VAT/SAT-ratio following adjustment as above and Bonferroni-correction of the p-value.CONCLUSION: Three cardiovascular-related proteins, cathepsin D, IL-1RA and growth hormone, were being associated with the distribution of abdominal adipose tissue using a discovery/validation approach. A meta-analysis of the two samples suggested that also a number of other cardiovascular-related proteins could be associated with an unfavorable abdominal fat distribution.
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38.
  • Lind, Lars, et al. (författare)
  • Relationships between carotid artery intima-media thickness and echogenicity and body composition using a new magnetic resonance imaging voxel-based technique
  • 2021
  • Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 16:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Background We evaluated how carotid artery intima-media thickness (IMT) and the echogenicity of the intima-media (IM-GSM), measured by ultrasound, were related to body composition, evaluated by both traditional imaging techniques, as well as with a new voxel-based "Imiomics" technique. Methods In 321 subjects all aged 50 years in the POEM study, IMT and IM-GSM were measured together with a DXA scan for determination of fat and lean mass. Also a whole-body MRI scan was performed and the body volume was divided into >1 million voxels in a standardized fashion. IMT and IM-GSM were related to each of these voxels to create a 3D-view of how these measurements were related to size of each part of the body. Results IM-GSM was inversely related to almost all traditional measurements of body composition, like fat and lean mass, liver fat, visceral and subcutaneous fat, but this was not seen for IMT. Using Imiomics, IMT was positively related to the intraabdominal fat volume, as well of the leg skeletal muscle in women. In males, IMT was mainly positively related to the leg skeletal muscle volume. IM-GSM was inversely related to the volume of the SAT in the upper part of the body, leg skeletal muscle, the liver and intraabdominal fat in both men and women. Conclusion The voxel-based Imiomics technique provided a detailed view of how the echogenicity of the carotid artery wall was related to body composition, being inversely related to the volume of the major fat depots, as well as leg skeletal muscle.
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39.
  • Lind, Lars, et al. (författare)
  • Voxel-wise Study of Cohort Associations in Whole-Body MRI : Application in Metabolic Syndrome and Its Components.
  • 2020
  • Ingår i: Radiology. - : Radiological Society of North America (RSNA). - 0033-8419 .- 1527-1315. ; 294:3, s. 559-567
  • Tidskriftsartikel (refereegranskat)abstract
    • BackgroundThe metabolic syndrome is related to obesity and ectopic fat distribution.PurposeTo investigate whether an image analysis approach that uses image registration for whole-body voxel-wise analysis could provide additional information about the relationship between metabolic syndrome and body composition compared with traditional image analysis.Materials and MethodsWhole-body quantitative water-fat MRI was performed in a population-based prospective study on obesity, energy, and metabolism between October 2010 and November 2016. Fat mass was measured with dual-energy x-ray absorptiometry (DXA). Whole-body voxel-wise analysis of tissue volume and fat content was applied in more than 2 million voxels from the whole-body examinations by automated interindividual deformable image registration of the water and fat MRI data. Metabolic syndrome was diagnosed by the harmonized National Cholesterol Education Program criteria. Two-tailed t tests were used and P values less than .05 were considered to indicate statistical significance.ResultsThis study evaluated 167 women and 159 men (mean age, 50 years) by using voxel-wise analysis. Metabolic syndrome (13.5%; 44 of 326) was related to traditional measurements of fat distribution, such as total fat mass at DXA, visceral and subcutaneous adipose tissue, and liver and pancreatic fat at MRI. Voxel-wise analysis found metabolic syndrome related to liver, heart, and perirenal fat volume; fat content in subcutaneous fat in the hip region in both sexes; fatty infiltration of leg muscles in men, especially in gluteus maximus; and pericardial and aortic perivascular fat mainly in women. Sex differences in associations with subcutaneous adipose tissue were identified. In women, metabolic syndrome diagnosis was linked to regional differences in associations to adipose tissue volumes in upper versus lower body, and dorsal versus ventral abdominal depots. In men similar gradients were only seen in individual components.ConclusionIn addition to showing the relationships between metabolic syndrome and body composition in a detailed and intuitive fashion in the whole body, the voxel-wise analysis provided additional information compared with traditional image analysis.
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40.
  • Lind, P. Monica, 1957-, et al. (författare)
  • Serum levels of perfluoroalkyl substances (PFAS) and body composition - A cross-sectional study in a middle-aged population
  • 2022
  • Ingår i: Environmental Research. - : Elsevier. - 0013-9351 .- 1096-0953. ; 209, s. 112677-
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: It has been suggested that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors with a potential to influence fat mass. Objective: The primary hypothesis tested was that we would find positive relationships for PFAS vs measures of adiposity. Methods: In 321 subjects all aged 50 years in the POEM study, five PFAS (perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA)) were measured in serum together with a Dual-energy X-ray absorptiometry (DXA) scan for determination of fat and lean mass. Whole-body magnetic resonance imaging scan was performed and the body was divided into >1 million voxels. Voxel-wise statistical analysis was carried out by a novel method denoted Imiomics. Results: PFOS and PFHxS, did not show any consistent associations with body composition. However, PFOA, and especially PFNA and PFDA, levels were inversely related to most traditional measures reflecting the amount of fat in women, but not in men. In the Imiomics analysis of tissue volume, PFDA and PFNA levels were inversely related to the volume of subcutaneous fat, mainly in the arm, trunk and hip regions in women, while no such clear relationship was seen in men. Also, the visceral fat content of the liver, the pericardium, and the gluteus muscle were inversely related to PFDA and PFNA in women. Discussion: Contrary to our hypothesis, some PFAS showed inverse relationships vs measurements of adiposity. Conclusion: PFOS and PFHxS levels in plasma did not show any consistent associations with body composition, but PFOA, and especially PFNA and PFDA were inversely related to multiple measures reflecting the amount of fat, but in women only.
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41.
  • Lundström, Elin, et al. (författare)
  • PET/MRI of glucose metabolic rate, lipid content and perfusion in human brown adipose tissue
  • 2021
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 11
  • Tidskriftsartikel (refereegranskat)abstract
    • This study evaluated the MRI-derived fat fraction (FF), from a Cooling-reheating protocol, for estimating the cold-induced brown adipose tissue (BAT) metabolic rate of glucose (MRglu) and changes in lipid content, perfusion and arterial blood volume (V-A) within cervical-supraclavicular fat (sBAT). Twelve volunteers underwent PET/MRI at baseline, during cold exposure and reheating. For each temperature condition, perfusion and V-A were quantified with dynamic [O-15]water-PET, and FF, with water-fat MRI. MRglu was assessed with dynamic [F-18]fluorodeoxyglucose-PET during cold exposure. sBAT was defined using anatomical criteria, and its subregion sBAT(HI), by MRglu>11 mu mol/100 cm(3)/min. For all temperature conditions, sBAT-FF correlated negatively with sBAT-MRglu (rho <=- 0.87). After 3 h of cold, sBAT-FF decreased (- 2.13 percentage points) but tended to normalize during reheating although sBAT(HI)-FF remained low. sBAT-perfusion and sBAT-V-A increased during cold exposure (perfusion:+5.2 ml/100 cm(3)/min, V-A:+4.0 ml/100 cm(3)). sBAT-perfusion remained elevated and sBAT-V-A normalized during reheating. Regardless of temperature condition during the Cooling-reheating protocol, sBAT-FF could predict the cold-induced sBAT-MRglu. The FF decreases observed after reheating were mainly due to lipid consumption, but could potentially be underestimated due to intracellular lipid replenishment. The influence of perfusion and V-A, on the changes in FF observed during cold exposure, could not be ruled out.
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42.
  • Pal, Subhash Chandra, et al. (författare)
  • Multi-level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA towards Diagnosis of Cerebrovascular Disorders
  • 2024
  • Ingår i: IEEE Transactions on Nanobioscience. - : IEEE. - 1536-1241 .- 1558-2639. ; 23:1, s. 167-175
  • Tidskriftsartikel (refereegranskat)abstract
    • Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important pre-processing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine- grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.
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43.
  • Pal, Subhash, et al. (författare)
  • Segmentation of Major Cerebral Vessel from MRA images and Evaluation using U-Net Family
  • 2022
  • Ingår i: 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665473804 ; , s. 235-238
  • Konferensbidrag (refereegranskat)abstract
    • Arterial cerebral vessel assessment is critical for thediagnosis of patients with cerebrovascular disease e.g., hypertension, Intracranial aneurysms, and dementia. Magnetic resonance angiography is a primary imaging technique for diagnosing cerebrovascular diseases. There are many Convolutional neuralnetworks (CNN) based methods for cerebral vessel segmentation but lack to identify the target vessels and understand the arterial tree structure for diagnosis and endovascular surgical planning.In the present study, we generated annotations for major vesselsegmentation and analyzed fully automatic segmentation of major vessels using state-of-the-art U-Net based deep learning models. Computer-aided major cerebral vessel segmentation incorporatedinto clinical practice may help speed up the diagnosis of time-critical vessel anomalies and help find important bio-markers for neurological dysfunction. We validated and compared U-Net based models for volumetric segmentation and predictionof cerebral arteries and it could be done in real-time withoutany image pre-processing.
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44.
  • Sjöholm, Therese, et al. (författare)
  • A whole-body diffusion MRI normal atlas : development, evaluation and initial use
  • 2023
  • Ingår i: Cancer Imaging. - : BioMed Central (BMC). - 1740-5025 .- 1470-7330. ; 23:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task.Methods: Multi-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture.Results: Sex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4-48% at 1.5T, 6-70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision.Conclusions: Whole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.
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45.
  • Sjöholm, Therese (författare)
  • Cancer imaging and image analysis methods in whole-body MRI and PET/MRI
  • 2023
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Diagnostic medical imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) can provide structural and functional assessments of the whole body. This has great value for potentially systemic diseases such as cancer. To take advantage of the enormous amount of data provided by current imaging systems, improvements in whole-body imaging protocols and advancements in image analysis methods are however needed. This thesis aims to develop advanced imaging and image analysis methods for the purpose of tumour characterisation in MRI and combined PET/MRI whole-body image datasets. Early prediction of progression free survival (PFS) and overall survival (OS) in patients with relapsed/refractory (r/r) large B-cell lymphoma (LBCL) undergoing chimeric antigen receptor (CAR) T-cell therapy was assessed using whole-body PET/MRI pre- and post-therapy. Reference standard manual segmentations of tumours and non-malignant lymphoid tissue were used, and an extended set of semi-quantitative and quantitative PET/MRI metrics was extracted. Predictive PET/MRI metrics included the metabolic tumour volume (MTV), tumour apparent diffusion coefficient (ADC) and 18F-fluorodeoxyglucose (FDG) uptake in non-malignant bone marrow. To enable automated image analysis, deformable image registration was used to create multiparametric normal atlases of healthy volunteers examined with whole-body FDG PET, diffusion weighted imaging (DWI) MRI and water-fat MRI. To improve the geometric accuracy of DWI in the normal atlas, the reverse polarity gradient (RPG) distortion correction method was evaluated. RPG increased the geometrical alignment between DWI and structural images acquired in the same scan session, with little effect on healthy tissue ADC. It was further shown that healthy tissue assessments in atlas space was possible, with the normal atlas employed to study voxel-wise correlations between ADC and age across the whole body, confirming results from a manual segmentation approach. As proof of concept, a probabilistic atlas based approach was successfully used for segmentation of suspected malignant disease in FDG PET data and detection of liver fat infiltration in fat fraction (FF) MRI data. Lastly, using a cohort of r/r LBCL patients, statistical deviations between patient and normal atlas DWI data included as input in a deep learning based model, improved its performance for automated tumour segmentation.
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46.
  • Sjöholm, Therese, et al. (författare)
  • Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients
  • 2022
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g. lesion detection and segmentation. In this work we assess the performance of the reverse polarity gradient (RPG) method for correction of WB-DWI geometric distortion. Multi-station DWI and structural magnetic resonance imaging (MRI) data of healthy controls were acquired at 1.5T (n = 20) and 3T (n = 20). DWI data was distortion corrected using the RPG method based on b = 0 s/mm(2) (b0) and b = 50 s/mm(2) (b50) DWI acquisitions. Mutual information (MI) between low b-value DWI and structural data increased with distortion correction (P < 0.05), while improvements in region of interest (ROI) based similarity metrics, comparing the position of incidental findings on DWI and structural data, were location dependent. Small numerical differences between non-corrected and distortion corrected apparent diffusion coefficient (ADC) values were measured. Visually, the distortion correction improved spine alignment at station borders, but introduced registration-based artefacts mainly for the spleen and kidneys. Overall, the RPG distortion correction gave an improved geometric accuracy for WB-DWI data acquired at 1.5T and 3T. The b0- and b50-based distortion corrections had a very similar performance.
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47.
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48.
  • Strand, Robin, 1978-, et al. (författare)
  • Relationships Between Plasma Levels And Six Proinflammatory Interleukins And Body Composition Using A New Magnetic Resonance Imaging Voxel-based Technique
  • 2021
  • Ingår i: Cytokine: X. - : Elsevier BV. - 2590-1532. ; 3:1, s. 100050-
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Obesity has previously been linked to inflammation. Here we investigated how plasma levels of six interleukins were related to body fat distributionMethods: In 321 subjects, all aged 50 years, in the population-based POEM study (mean BMI 26-27 kg/m2), six interleukins were measured together with a DXA scan for determination of fat and lean mass. Also a whole-body magnetic resonance imaging (MRI) scan, in which fat content measurements were acquired in >1 million voxels was performed. Interleukin levels were related to each of these voxels by the voxel-based technique “imiomics” to create a 3D-view of how these measurements were related to size of each part of the body.Results: Levels of IL-1RA and IL-6 were related to traditional DXA and MRI measurements of adipose tissue at all locations. Neither IL-6R, nor IL-8 or IL-18, showed any consistent significant relationships vs the traditional measurements of body composition, while IL-16 showed relationships being of borderline significance. The imiomics evaluation further strengthen the view that IL-1RA and IL-6 were related to subcutaneous adipose tissue (SAT), as well to ectopic fat distribution. In women, IL-16 levels were weakly related to expansion of SAT in the upper part of the body, while on the contrary, IL-8 levels were related to a reduction of SAT volume.Conclusion: Of the six evaluated interleukins, plasma IL-1RA and IL-6 levels were related to the amount of adipose tissue in all parts of the body, while a diverse picture was seen for other interleukins, suggesting that different interleukins are related to fat distribution in different ways.
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49.
  • Tarai, Sambit, et al. (författare)
  • Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors
  • 2024
  • Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 10:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end -to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.g. size, shape, location). Subsequently, the TPDM is integrated with a state-of-the-art 3D segmentation network along with the original PET/CT or PET/MR images. This aims to produce more meaningful tumor segmentation masks compared to using the baseline 3D segmentation network alone. The proposed method was evaluated on three independent cohorts (autoPET, CAR-T, cHL) of images containing different cancer forms, obtained with different imaging modalities, and acquisition parameters and lesions annotated by different experts. The evaluation demonstrated the superiority of our proposed method over the baseline model by significant margins in terms of Dice coefficient, and lesion-wise sensitivity and precision. Many of the extremely small tumor lesions (i.e. the most difficult to segment) were missed by the baseline model but detected by the proposed model without additional false positives, resulting in clinically more relevant assessments. On average, an improvement of 0.0251 (autoPET), 0.144 (CAR-T), and 0.0528 (cHL) in overall Dice was observed. In conclusion, the proposed TPDM-based approach can be integrated with any state-of-the-art 3D UNET with potentially more accurate and robust segmentation results.
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