Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields-Reference-Cited by-同舟云学术

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Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields

Published:2024-12 Issue: Volume:179 Page:111298
ISSN:0030-3992
Container-title:Optics & Laser Technology
language:en
Short-container-title:Optics & Laser Technology

Author:

Xia Zhen,Liao Miao,Di Shuanhu,Zhao Yuqian,Liang Wei,Xiong Neal N.^ORCID

Publisher

Elsevier BV

Reference56 articles.

1. TWD-R: A three-way decision approach based on regret theory in multi-scale decision information systems;Huang;Inform. Sci.,2021

2. A two-stage approach for automatic liver segmentation with faster R-CNN and DeepLab;Tang;Neural Comput. Appl.,2020

3. Classification of hepatic lesions using the matching metric;Adcock;Comput. Vis. Image Underst.,2014

4. Lim-Net: Lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images;Kushnure;Biomed. Signal Process. Control,2023

5. A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans;Chen;Comput. Biol. Med.,2023

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