Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, Central Nervous System； Tumor ；segmentation
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.
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