CRLM-GAN: a feature-constrained GAN-based deep learning framework for multi-parametric MRI-based segmentation of colorectal liver metastases before and after chemotherapy
Shao-Jun Xia, Hai-Bin Zhu, Xiao-Lei Gu, Jing Bao, Anlan Sun, Yong Cui, Xiao-Ting Li, Ying-Shi Sun · 2025
Read CRLM-GAN: a feature-constrained GAN-based deep learning framework for multi-parametric MRI-based segmentation of colorectal liver metastases before and after chemotherapy by Shao-Jun Xia, Hai-Bin Zhu, Xiao-Lei Gu, Jing Bao, Anlan Sun, Yong Cui, Xiao-Ting Li, Ying-Shi Sun (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12821965.txt.
Description
Shao-Jun Xia and a team of co-authors build an artificial intelligence tool that outlines liver tumors on scans. Their study appeared in twenty twenty-five. The tumors are spread from bowel cancer, and drawing their borders by hand is slow, above all after drug treatment blurs the edges.
The team trained the tool on scan images from patients taken before and after chemotherapy. The design pits two networks against each other, one drawing the outline and one judging it, with extra features to guide the drawing. They tested it within single scan types and across types. The goal is a faster, steadier way to measure these tumors, which helps doctors judge whether treatment is working and plan what comes next.
Catalog record
| CREATOR | Shao-Jun Xia, Hai-Bin Zhu, Xiao-Lei Gu, Jing Bao, Anlan Sun, Yong Cui, Xiao-Ting Li, Ying-Shi Sun |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |