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Iterative Chan-Vese refinement actually degrades segmentation accuracy compared to raw Otsu thresholding, exposing how uncalibrated stopping tolerances create illusory algorithmic improvements over poorly initialized baselines.
Calibrating decision thresholds against different human annotators shifts segmentation performance by over 2.5 Dice points on identical predictions, revealing that common benchmark "improvements" are frequently artifacts of observer selection.