Segmentation Features for CT Scans: A Taxonomy
DOI:
https://doi.org/10.61841/x1wf5311Keywords:
Segmentation, Medical Image, CT Scans, Image Features, Image Representation, Deep LearningAbstract
Image segmentation is a crucial task in medical imaging applications. Segmentation can aid in several medical acts, such as planning therapy radiation, automatic labeling of anatomical structures, lesion detection, surgical intervention, virtual surgery simulation, intra-surgery navigation, etc. Despite works done in imaging segmentation, it stays challenging because of problems linked to image acquisition conditions and artifacts such as low-contrast images, similar intensities with adjacent objects of interest, noise, etc. In the last decade a big variety of algorithms was proposed for this aim. A widely used recent method consists of using artificial intelligence to achieve the segmentation task based on present labeled images. In this paper we review the relevant proposed approaches in medical imaging segmentation, with a focus on the methods based on AI and especially the deep learning methods. We summarize the accurate algorithms in a taxonomy followed by a comparison discussion. Finally, we present the new research directions that aim at overcoming current limitations in the segmentation task.
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