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Medical Image Segmentation based on U-Net: A Review

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Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.
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Document Type: Research Article

Affiliations: Engineering Research Center of Molecular & Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710071, China

Publication date: March 1, 2020

This article was made available online on March 13, 2020 as a Fast Track article with title: "Medical Image Segmentation based on U-Net: A Review".

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

    IS&T's JIST-first publication option allows authors wishing to present their work at conferences, but have a journal citation for their paper, to submit a paper to JIST that follows the same rigorous peer-review vetting and publication process as traditional JIST articles, but with the benefit of a condensed time-to-publication time frame and guaranteed conference presentation slot.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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