MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation

Key points

• Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which require each prediction to be paired with a ground-truth mask.1
• MID builds upon the observation that segmentation quality is a property of the joint image-mask distribution: a mask may be anatomically plausible in isolation yet incorrect for the image it accompanies.1
• MID compares the joint distribution of images and masks between a reference set and a set of predictions, without requiring sample-wise correspondence.1
• MID also detects distributional shifts across modalities and anatomies, identifies image-mask misalignment through controlled shuffling experiments, and produces reliable model rankings across real segmentation models.1

Sources (1)

• [1]MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 08:12 PM

  We introduce MID (Mask-Image Distributional Divergence), a distribution-level evaluation metric for medical image segmentation.

  Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which require each prediction to be paired with a ground-truth mask.

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