Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study

Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study

Abstract Background Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning. Purpose To develop a deep learning (DL) framework for efficient segmentation of PSTs from multi-sequence MRI. Ma...

Full description

Saved in:
Bibliographic Details
Main Authors: Ping Yin, Weidao Chen, Qianrui Fan, Ruize Yu, Xia Liu, Tao Liu, Dawei Wang, Nan Hong
Format: Article
Language:English
Published: BMC 2025-03-01
Series:Cancer Imaging
Subjects:
Pelvic and sacral tumors
Magnetic resonance imaging
Deep learning
2.5D
Segmentation
Online Access:https://doi.org/10.1186/s40644-025-00850-8
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s40644-025-00850-8

Similar Items