Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917084706897920 |
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| author | Zhou, Andrew |
| author_facet | Zhou, Andrew |
| contents | Accurate segmentation of brain tumors is vital for diagnosis, surgical planning, and treatment monitoring. Deep learning has advanced on benchmarks, but two issues limit clinical use: no uncertainty estimates for errors and no segmentation of healthy brain structures around tumors for surgery. Current methods fail to unify tumor localization with anatomical context and lack confidence scores. This study presents an uncertainty-aware framework augmenting nnUNet with a channel for voxel-wise uncertainty. Trained on BraTS2023, it yields a correlation of 0.750 and RMSD of 0.047 for uncertainty without hurting tumor accuracy. It predicts uncertainty in one pass, with no extra networks or inferences, aiding clinical decisions. For whole-brain context, a unified model combines normal and cancer datasets, achieving a DSC of 0.81 for brain structures and 0.86 for tumor, with robust key-region performance. Combining both innovations gives the first model outputting tumor in natural surroundings plus an overlaid uncertainty map. Visual checks of outputs show uncertainty offers key insights to evaluate predictions and fix errors, helping informed surgical decisions from AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation Zhou, Andrew Computer Vision and Pattern Recognition Accurate segmentation of brain tumors is vital for diagnosis, surgical planning, and treatment monitoring. Deep learning has advanced on benchmarks, but two issues limit clinical use: no uncertainty estimates for errors and no segmentation of healthy brain structures around tumors for surgery. Current methods fail to unify tumor localization with anatomical context and lack confidence scores. This study presents an uncertainty-aware framework augmenting nnUNet with a channel for voxel-wise uncertainty. Trained on BraTS2023, it yields a correlation of 0.750 and RMSD of 0.047 for uncertainty without hurting tumor accuracy. It predicts uncertainty in one pass, with no extra networks or inferences, aiding clinical decisions. For whole-brain context, a unified model combines normal and cancer datasets, achieving a DSC of 0.81 for brain structures and 0.86 for tumor, with robust key-region performance. Combining both innovations gives the first model outputting tumor in natural surroundings plus an overlaid uncertainty map. Visual checks of outputs show uncertainty offers key insights to evaluate predictions and fix errors, helping informed surgical decisions from AI. |
| title | Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12801 |