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Deep Learning for Medical Image Segmentation

Deep Learning for Medical Image Segmentation

Kanchan Sarkar, Bohang Li
Copyright: © 2021 |Pages: 38
ISBN13: 9781799850717|ISBN10: 1799850714|EISBN13: 9781799850724
DOI: 10.4018/978-1-7998-5071-7.ch002
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MLA

Sarkar, Kanchan, and Bohang Li. "Deep Learning for Medical Image Segmentation." Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, IGI Global, 2021, pp. 40-77. https://doi.org/10.4018/978-1-7998-5071-7.ch002

APA

Sarkar, K. & Li, B. (2021). Deep Learning for Medical Image Segmentation. In S. Saxena & S. Paul (Eds.), Deep Learning Applications in Medical Imaging (pp. 40-77). IGI Global. https://doi.org/10.4018/978-1-7998-5071-7.ch002

Chicago

Sarkar, Kanchan, and Bohang Li. "Deep Learning for Medical Image Segmentation." In Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, 40-77. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5071-7.ch002

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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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