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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 Voxel Morph 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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- Ekström, Simon, 1991-, et al.
(författare)
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Fast graph-cut based optimization for practical dense deformable registration of volume images
- 2020
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Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier. - 0895-6111 .- 1879-0771. ; 84
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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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- Ekström, Simon, 1991-, et al.
(författare)
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Faster dense deformable image registration by utilizing both CPU and GPU
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Annan publikation (övrigt vetenskapligt/konstnärligt)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 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 a factor 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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- Ekström, Simon, 1991-, et al.
(författare)
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Faster dense deformable image registration by utilizing both CPU and GPU
- 2021
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Ingår i: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 8:1
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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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