Spatially-Aware Evaluation of Segmentation Uncertainty

• URL: http://arxiv.org/abs/2506.16589v1
• Date: Thu, 19 Jun 2025 20:24:57 GMT
• Title: Spatially-Aware Evaluation of Segmentation Uncertainty
• Authors: Tal Zeevi, Eléonore V. Lieffrig, Lawrence H. Staib, John A. Onofrey,
• Abstract summary: Uncertainty evaluation metrics treat voxels independently, ignoring spatial context and anatomical structure.<n>We propose three spatially aware metrics that incorporate structural and boundary information.<n>Our results demonstrate improved alignment with clinically important factors and better discrimination between meaningful and spurious uncertainty patterns.
• Score: 2.3272947684291116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and anatomical structure. As a result, they may assign identical scores to qualitatively distinct patterns (e.g., scattered vs. boundary-aligned uncertainty). We propose three spatially aware metrics that incorporate structural and boundary information and conduct a thorough validation on medical imaging data from the prostate zonal segmentation challenge within the Medical Segmentation Decathlon. Our results demonstrate improved alignment with clinically important factors and better discrimination between meaningful and spurious uncertainty patterns.

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