SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation

Jacqueline Isabel Bereska, Hamed Karimi, Reza Samavi

Keywords: Uncertainty, Conformal Prediction, Medical Image Segmentation

TL;DR: A novel uncertainty quantification method for medical image segmentation sensitive to critical anatomical interfaces allowing optimal surgery planning.

Abstract:

While Conformal Prediction provides statistical coverage guarantees, existing non-conformity measures fail to account for spatially varying importance of predictive uncertainty in medical image segmentation. In this paper, we incorporate spatial context near critical interfaces such as a vessel or critical organ in medical image segmentation. Our framework consists of three key components: (1) a base non-conformity score derived from segmentation model probabilities, (2) employing class-conditional calibration followed by a validation mechanism equipped with a distance-weighted scoring function that exponentially decays with distance from key interfaces, and (3) a prediction set construction method that preserves coverage guarantees while providing targeted uncertainty quantification in critical regions.
While our approach is generalizable to different scenarios, for validation purposes, we employ tumor segmentation in pancreatic adenocarcinoma imaging from multiple medical centers. Results demonstrate that our method achieves the desired coverage levels while generating prediction sets that adaptively expand near critical interfaces.

Primary Subject Area: Uncertainty Estimation

Secondary Subject Area: Segmentation

Paper Type: Methodological Development

Registration Requirement: Yes

Reproducibility: https://github.com/tailabTMU/SACP

Visa & Travel: Yes

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Submission Number: 238