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- Ahmad, Nouman, et al.
(författare)
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Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
- 2023
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Ingår i: BMC BIOINFORMATICS. - : BioMed Central (BMC). - 1471-2105. ; 24:1
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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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- Breznik, Eva
(författare)
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Image Processing and Analysis Methods for Biomedical Applications
- 2023
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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)
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Multiple comparison correction methods for whole-body magnetic resonance imaging
- 2020
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Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 7:1
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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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- Ekström, Simon, 1991-, et al.
(författare)
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Deformable Image Registration of Volumetric Whole-body MRI: An Evaluation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Whole-body imaging presents a variety of interesting applications and combining these information-rich images with image registration enables detailed large-scale analysis. Whole-body image registration, with the large variability present in human anatomy, introduces a range of challenges that need to be dealt with. This paper aims to present two new extensions to a previously published registration method based on compositive updates and voxel-wise regularization. The new extensions are evaluated against a previously presented pipeline for whole-body registration and a learning-based approach using the VoxelMorph framework. The methods are evaluated on Dice overlap, smoothness of produced displacement fields, and the inverse consistency error. The presented extensions are shown to improve upon previous method both in terms of computation time and registration quality. The voxel-wise regularization produces a mean Dice overlap of 0.828 for the 10 segmented regions and a mean computation time of 320 seconds per subject. The learning-based approach had an inference time of only 3 seconds but a training time of 16 hours per reference subject. This approach produced a mean Dice overlap of only 0.797 but it was shown that the issues in overlap score were limited to the kidneys. In conclusion, both the extensions and VoxelMorph has presented great promise for the task of whole-body registration compared to previous method. However, the choice of method will be highly dependent upon the task. VoxelMorph provides results of lower quality and reduced flexibility but a computation time of only a few seconds.
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- Ekström, Simon, 1991-
(författare)
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Efficient GPU-based Image Registration : for Detailed Large-Scale Whole-body Analysis
- 2020
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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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