SHIFT-QA: target-calibrated accept-or-review quality assurance for cardiac MRI segmentation under pathology shift
Abstract
Abstract Background: Automated cardiac magnetic resonance segmentation can fail after deployment outside development data, yet conventional validation usually reports average accuracy rather than whether an individual patient result should be accepted automatically or reviewed. We developed SHIFT-QA, a target-calibrated accept-or-review quality-assurance framework for medical image segmentation under pathology shift.
Methods:: SHIFT-QA combines ensemble-derived reliability evidence, including predictive uncertainty, mask disagreement, volume variability, and downstream-measurement instability, into a patient-level risk score. A labeled target-calibration subset is used to select an acceptance threshold before target-test labels are accessed. We implemented a local proof-of-concept on the Automated Cardiac Diagnosis Challenge (ACDC) cardiac magnetic resonance dataset using hypertrophic cardiomyopathy (HCM) as a pathologyheld-out target cohort. The audited cohort contained 100 patients and 200 end-diastolic/endsystolic records. Source training, source validation, target calibration, and target testing were patient-disjoint.
Results:: A lightweight two-dimensional U-Net reached a validation mean foreground Dice of 0.822 after continuation training. Failure-rule diagnosis showed that a strict minimum-class Dice rule was overly conservative for HCM calibration. The frozen operational rule defined failure as patient mean foreground Dice below 0.75 or absolute ejection-fraction…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 100 participants or records.
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