A Semi-Supervised Multi-Region Segmentation Framework of Bladder Wall and Tumor with Wall-Enhanced Self-Supervised Pre-Training

A Semi-Supervised Multi-Region Segmentation Framework of Bladder Wall and Tumor with Wall-Enhanced Self-Supervised Pre-Training

Bladder cancer is a prevalent and highly recurrent malignancy within the urinary tract. The accurate segmentation of the bladder wall and tumor in magnetic resonance imaging (MRI) is a crucial step in distinguishing between non-muscle-invasive and muscle-invasive types of bladder cancer, which plays...

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Bibliographic Details
Main Authors: Jie Wei, Yao Zheng, Dong Huang, Yang Liu, Xiaopan Xu, Hongbing Lu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Bioengineering
Subjects:
bladder cancer
magnetic resonance image
self-supervised learning
multi-region segmentation
semi-supervised learning
Online Access:https://www.mdpi.com/2306-5354/11/12/1225
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