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Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation

Andrew Zhou

TL;DR

The paper tackles the gap between high Dice scores on benchmarks and real-world clinical usability by introducing a voxel-wise uncertainty prediction channel to a 3D nnU-Net and by extending segmentation to jointly cover tumor and surrounding healthy brain structures. It proposes a single-pass uncertainty mechanism and a unified cancer/brain segmentation model trained on BraTS2023 and OASIS-1, coupled with a loss that couples segmentation accuracy to uncertainty calibration via RMSD and correlation terms. The approach yields competitive tumor segmentation and strong uncertainty alignment (UM1 CORR ≈ 0.75) while enabling visualization of tumors in context with anatomical structures and an overlay of uncertainty to guide surgeons. The method reduces the need for multiple inference passes and provides clinically meaningful confidence guidance, potentially improving surgical planning and decision-making in neuro-oncology.

Abstract

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.

Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation

TL;DR

The paper tackles the gap between high Dice scores on benchmarks and real-world clinical usability by introducing a voxel-wise uncertainty prediction channel to a 3D nnU-Net and by extending segmentation to jointly cover tumor and surrounding healthy brain structures. It proposes a single-pass uncertainty mechanism and a unified cancer/brain segmentation model trained on BraTS2023 and OASIS-1, coupled with a loss that couples segmentation accuracy to uncertainty calibration via RMSD and correlation terms. The approach yields competitive tumor segmentation and strong uncertainty alignment (UM1 CORR ≈ 0.75) while enabling visualization of tumors in context with anatomical structures and an overlay of uncertainty to guide surgeons. The method reduces the need for multiple inference passes and provides clinically meaningful confidence guidance, potentially improving surgical planning and decision-making in neuro-oncology.

Abstract

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.

Paper Structure

This paper contains 21 sections, 5 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Research Gap and Proposed Solution
  4. Methodology
  5. Model Development
  6. Dataset
  7. Uncertainty Prediction Channel
  8. Uncertainty Target Construction
  9. Loss Function
  10. Evaluation Metrics
  11. Results and Discussion
  12. Model Uncertainty Performance
  13. CM Models Uncertainty Prediction Visualization
  14. Model Segmentation Performance
  15. DSC of Individual Brain Regions
  16. ...and 6 more sections

Figures (7)

  • Figure 1: 3D nnU-Net architecture.
  • Figure 2: Proposed uncertainty prediction process.
  • Figure 3: Visual comparison of CM1 segmentation and uncertainty prediction results.
  • Figure 4: Comparison of DSC of detailed brain regions in unified models.
  • Figure 5: Case 1: Prediction matches reality with uncertainty highlighting boundary.
  • ...and 2 more figures