Deep Learning for Medical Image Segmentation

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Author(s): Kanchan Sarkar (Indian Institute of Technology, Bombay, India)and Bohang Li (Peking University, China)
Copyright: 2023
Pages: 31
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch044

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

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Abstract

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.

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Deep Learning for Medical Image Segmentation

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