Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedback

• URL: http://arxiv.org/abs/2511.02576v1
• Date: Tue, 04 Nov 2025 13:53:10 GMT
• Title: Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedback
• Authors: Alix de Langlais, Benjamin Billot, Théo Aguilar Vidal, Marc-Olivier Gauci, Hervé Delingette,
• Abstract summary: We present SCORE, a weakly supervised framework that learns to refine mask predictions only using light feedback during training.<n>We demonstrate SCORE on humerus CT scans, where it considerably improves initial predictions and achieves performance on par with existing refinement methods.
• Score: 1.8082075562656847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Delineating anatomical regions is a key task in medical image analysis. Manual segmentation achieves high accuracy but is labor-intensive and prone to variability, thus prompting the development of automated approaches. Recently, a breadth of foundation models has enabled automated segmentations across diverse anatomies and imaging modalities, but these may not always meet the clinical accuracy standards. While segmentation refinement strategies can improve performance, current methods depend on heavy user interactions or require fully supervised segmentations for training. Here, we present SCORE (Segmentation COrrection from Regional Evaluations), a weakly supervised framework that learns to refine mask predictions only using light feedback during training. Specifically, instead of relying on dense training image annotations, SCORE introduces a novel loss that leverages region-wise quality scores and over/under-segmentation error labels. We demonstrate SCORE on humerus CT scans, where it considerably improves initial predictions from TotalSegmentator, and achieves performance on par with existing refinement methods, while greatly reducing their supervision requirements and annotation time. Our code is available at: https://gitlab.inria.fr/adelangl/SCORE.

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