A Review of Advancements and Challenges in Liver Segmentation

被引：1

作者：

Wei, Di ^{[1
]}

Jiang, Yundan ^{[1
]}

Zhou, Xuhui ^{[1
]}

Wu, Di ^{[1
]}

Feng, Xiaorong ^{[1
]}

机构：

[1] Sun Yat Sen Univ, Affiliated Hosp 8, Dept Radiol, 3025 Middle Shennan Rd, Shenzhen 518033, Peoples R China

来源：

JOURNAL OF IMAGING | 2024年 / 10卷 / 08期

关键词：

liver segmentation; medical imaging; deep learning; convolutional neural networks; fully; convolutional networks; U-Net; automated segmentation; CT;

D O I：

10.3390/jimaging10080202

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

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