Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedback
This work addresses the need for more efficient and less labor-intensive segmentation refinement in medical imaging, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of improving automated medical image segmentation accuracy without heavy supervision by introducing SCORE, a weakly supervised framework that uses region-wise quality scores and error labels to refine mask predictions, achieving performance comparable to existing refinement methods on humerus CT scans while significantly reducing annotation time.
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.