深度学习在医学图像分割中应用
首发时间:2016-05-26
摘要:医学图像分割是图像分割的一个重要领域,它在图像中自动分割出感兴趣的区域方面扮演着及其重要的角色。由于人体的器官组织是可发生形变的,而且影像上相邻灰度差别也很小,这些因素加大了图像分割的难度。因此,将感兴趣的区域准确的分割出来,需要对特征提取提出更高的要求。深度学习因其具有多隐层,能够自动学习有用的特征的特性,在特征的提取以及最终的分割结果在实际应用中效果很好。本文先简述了医学图像分割的重要性,以及深度学习的发展历程,再详细概述了深度学习的方法以及在医学图像分割中的应用。
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Review of deep learning applied in medical image segmentation
Abstract: Medical image segmentation is an important area of image segmentation, and it plays an important role in automatic segmentation the region of interest image. Due to the body's organs' deformation issues and the gray level's difference on the neighboring region is very small, these factors increase the difficulty of image segmentation. As a result, in order to segment the region of interest accurately, it puts forward high demands on feature extraction. It is because that deep learning has many hidden layers, and can automatically learn useful characteristic features, the effect on feature extraction and the final segmentation result in practice is very good. This article first briefly describes the importance of medical image segmentation, and the development of deep learning, and then a detailed overview of the deep learning method and its application in medical image segmentation.
Keywords: medical image segmentation feature extraction deep learning
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