Applications of Image Segmentation Techniques in Medical Images

Yang-yang Hou
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Abstract

Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.
图像分割技术在医学图像中的应用
图像分割是医学图像处理任务中的一个重要研究方向,也是计算机视觉领域的一项具有挑战性的任务。目前,已有很多图像分割方法,包括传统的分割方法和基于深度学习的分割方法。通过对医学图像分割领域现状的了解和学习,本文对其进行了系统梳理。首先,简要介绍了阈值法、区域法、图切法等传统图像分割方法,重点介绍了基于深度学习的常用网络架构,如 CNN、FCN、U-Net、SegNet、PSPNet、Mask R-CNN。同时,阐述了其在医学图像分割中的应用。最后,讨论了基于深度学习的医学图像分割技术面临的挑战和发展机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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