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• W4211227762 abstract "Advancing radiology towards quantitative interpretation relies on innovating with image processing algorithms for context navigation (e.g., “Does this image contain a tumor?”), object localization (”Where is the spleen located?”), and segmentation (e.g., “Which voxels in the image belong to the hippocampus?”). In this chapter, we focus on the most invasive of these questions – segmentation. An algorithm that yields a segmentation of a medical image assigns each voxel in a target medical image to an object class (either deterministically or probabilistically). The results of a segmentation process can be used to address a wide variety of clinical problems from volumetry and shape analysis to radiomics and image-guided interventions. Historically, human efforts for capturing medical image segmentations (e.g., labelers or, herein, raters) have been remarkably resource intensive and subject to high degrees of interrater variability. Hence, medical images paired with their segmentations (so-called maps, charts, or, herein, atlases) are treasured resources. Creating approaches to learn algorithms based on an available and/or feasible collection of atlases has been at the forefront of medical image computing research for the better part of a quarter century. An intuitive and effective family of methods for using existing atlases to create a new algorithm is known as multiatlas segmentation. The core idea underlying multiatlas segmentation is that differences in anatomy as seen through medical images are relatively small and can be compensated through image registration. Hence, the segmentation of an atlas registered to an unseen target represents a reasonable estimation of the true segmentation for that target. When the registration process is repeated for multiple atlases, one would achieve multiple reasonable estimates of a segmentation. The field of multiatlas segmentation centers on capturing and resolving the uncertainty associated with multiple segmentation estimates and optimizing the preprocessing, registration, information integration, and postprocessing steps needed to address complexities of specific anatomies, imaging modalities, and clinical context. The intent of this chapter is to provide historical context for interpretation of modern multiatlas segmentation methods while offering a consistent notation to compare and contrast the evolving literature. Notably, while we highlight salient innovations both from practical and theoretical perspectives, this chapter does not seek to provide a comprehensive literature review." @default.
• W4211227762 created "2022-02-13" @default.
• W4211227762 creator A5012797975 @default.
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• W4211227762 date "2020-01-01" @default.
• W4211227762 modified "2023-09-26" @default.
• W4211227762 title "Multiatlas segmentation" @default.
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