Variational Models and Their Combinations with Deep Learning in Medical Image Segmentation: A Survey

Luying Gui (), Jun Ma () and Xiaoping Yang ()
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Luying Gui: Nanjing University of Science and Technology, Department of Mathematics
Jun Ma: Nanjing University of Science and Technology, Department of Mathematics
Xiaoping Yang: Nanjing University, Department of Mathematics

Chapter 27 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 1001-1022 from Springer

Abstract: Abstract Image segmentation means to partition an image into separate meaningful regions. Segmentation in medical images can extract different organs, lesions, and other regions of interest, which helps in subsequent disease diagnosis, surgery planning, and efficacy assessment. However, medical images have many unavoidable interference factors, such as imaging noise, artificial artifacts, and mutual occlusion of organs, which make accurate segmentation highly difficult. Incorporating prior knowledge and image information into segmentation model based on variational methods has proven efficient for more accurate segmentation. In recent years, segmentation based on deep learning has been significantly developed, and the combination of classical variational method-based models with deep learning is a hot topic. In this survey, we briefly review the segmentation methods based on a variational method making use of image information and regularity information. Subsequently, we clarify how the integration of variational methods into the deep learning framework leads to more precise segmentation results.

Keywords: Medical image segmentation; Variational models; Deep learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-98661-2_109

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