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Review of U-Net-Based Convolutional Neural Networks for Breast Medical Image Segmentation

Authors :
PU Qiumei, YIN Shuai, LI Zhengmao, ZHAO Lina
Source :
Jisuanji kexue yu tansuo, Vol 18, Iss 6, Pp 1383-1403 (2024)
Publication Year :
2024
Publisher :
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2024.

Abstract

U-Net and its variants have showcased exceptional performance in the domain of breast medical image segmentation. By employing a fully convolutional network (FCN) structure for semantic segmentation, the symmetrical structure of U-Net offers remarkable flexibility and adaptability. It can be tailored to diverse image segmentation tasks and challenges by adjusting network depth and incorporating new modules, leaving a significant impact on subsequent network designs. This paper aims to delve into the application of U-shaped convolutional networks in breast medical image segmentation, categorizing and summarizing U-shaped convolutional networks used for this purpose in recent years. It outlines the widely used breast medical image datasets and evaluation metrics, discusses common data augmentation techniques, and provides a detailed introduction to the network structure of the U-Net model along with traditional segmentation methods for breast medical images. Furthermore, it summarizes the improvements made to the U-Net network structure for breast medical image segmentation, including modifications like residual structures, multi-scale features, dilation mechanisms, attention mechanisms, skip connection mechanisms, and integration with Transformers. Finally, it addresses the current challenges and problems encountered in breast medical image segmentation and offers insights into future research directions.

Details

Language :
Chinese
ISSN :
16739418
Volume :
18
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Jisuanji kexue yu tansuo
Publication Type :
Academic Journal
Accession number :
edsdoj.3a69497d88f40d29926b0eeab2f4c2f
Document Type :
article
Full Text :
https://doi.org/10.3778/j.issn.1673-9418.2307069