A Comprehensive Deep Learning Approach for Multi-type Central Nervous System Tumor Segmentation Based on the 2021 WHO Classification

Motivation: CNS tumor segmentation is essential but remains limited by time-intensive methods and variability. Current automated approaches typically focus on single tumor types, lacking comprehensive clinical utility. Goal(s): Develop a unified deep learning model for accurate segmentation of 11 CNS tumor types using standard MRI sequences, addressing the limitations of existing single-tumor models. Approach: A 3D ResNet-Transformer framework was employed for feature extraction and attention-based enhancement. The model was trained and evaluated on a dataset combining public and hospital-sourced MRI scans. Results: The model achieved mean Dice scores of 0.896 demonstrating robust multi-tumor segmentation, though variability was noted in rarer types. Impact: This work advances automated, multi-tumor segmentation tools, enhancing clinical workflows and supporting consistent, efficient CNS tumor analysis.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1864
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

A Comprehensive Deep Learning Approach for Multi-type Central Nervous System Tumor Segmentation Based on the 2021 WHO Classification

Yue Qiang, Xiaoyun Liang, Ju Gao, Xibiao Yang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Brain Tumor Detection and Classification
article

A Comprehensive Deep Learning Approach for Multi-type Central Nervous System Tumor Segmentation Based on the 2021 WHO Classification

Yue Qiang, Xiaoyun Liang, Ju Gao, Xibiao Yang, A’ying Zeng, Weilin Yang, Hongjing Zhang, Liping Wang, Shuang Li, Liting Guo, Simin Zhang, Lang Wang, Yuchi Tian, Xiaorui Su, Shuang Tang, Y. Luo, Yuqi Jin
article en

Abstract

Motivation: CNS tumor segmentation is essential but remains limited by time-intensive methods and variability. Current automated approaches typically focus on single tumor types, lacking comprehensive clinical utility. Goal(s): Develop a unified deep learning model for accurate segmentation of 11 CNS tumor types using standard MRI sequences, addressing the limitations of existing single-tumor models. Approach: A 3D ResNet-Transformer framework was employed for feature extraction and attention-based enhancement. The model was trained and evaluated on a dataset combining public and hospital-sourced MRI scans. Results: The model achieved mean Dice scores of 0.896 demonstrating robust multi-tumor segmentation, though variability was noted in rarer types. Impact: This work advances automated, multi-tumor segmentation tools, enhancing clinical workflows and supporting consistent, efficient CNS tumor analysis.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 69%
Brain Tumor Detection and Classification
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