AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality

TL;DR

This paper introduces a Bayesian deep learning-based quality assessment method for auto-segmented contours in radiotherapy, enabling confident, ground-truth-independent predictions to improve clinical workflow efficiency.

Contribution

It presents a novel Bayesian ordinal classification approach with uncertainty calibration for auto-contour quality assessment in radiotherapy, effective across various data labeling scenarios.

Findings

Over 90% accuracy with minimal manual labels

93% correct quality predictions in 98% cases

Reduces manual review workload significantly

Abstract

Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on Online Adaptive Radiotherapy (OART). Leveraging Bayesian Ordinal Classification (BOC) and calibrated uncertainty thresholds, the method enables confident QA predictions without relying on ground truth contours or extensive manual labeling. Methods: We developed a BOC model to classify auto-contour quality and quantify prediction uncertainty. A calibration step was used to optimize uncertainty thresholds that meet clinical accuracy needs. The method was validated under three data scenarios: no manual labels, limited labels, and extensive labels. For rectum contours in prostate cancer, we applied geometric surrogate labels when manual labels were absent, transfer learning when limited, and direct supervision when ample labels were available. Results: The BOC model delivered robust performance across all scenarios. Fine-tuning with just 30 manual labels and calibrating with 34 subjects yielded over 90% accuracy on test data. Using the calibrated threshold, over 93% of the auto-contours' qualities were accurately predicted in over 98% of cases, reducing unnecessary manual reviews and highlighting cases needing correction. Conclusion: The proposed QA model enhances contouring efficiency in OART by reducing manual workload and enabling fast, informed clinical decisions. Through uncertainty quantification, it ensures safer, more reliable radiotherapy workflows.