Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation

2026-05-29

AI Summary

Clinical CT images often suffer from degradations such as noise, low resolution, and contrast variations, which significantly impair segmentation performance. To address this challenge, this work proposes RAMP, a novel framework that systematically integrates clinically motivated multi-degradation augmentation strategies. Specifically, RAMP enhances model robustness by combining anatomically constrained spatial perturbations, CT-specific intensity transformations, and stochastic combinations of multiple degradations, building upon the nnU-Net architecture. Evaluated on both a five-organ dataset and Abdomen1K, RAMP achieves average Dice scores of 0.753 and 0.789, respectively, on degraded images, reducing the robustness gap to only 0.064 and 0.070. These results demonstrate that RAMP substantially mitigates segmentation collapse under severe image degradation.

Abstract

Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts. This instability can limit reliable deployment in real-world medical imaging workflows. We propose Robustness via Augmented Multi-corruption Pipeline (RAMP), a robustness-oriented augmentation framework for CT segmentation. RAMP combines anatomically constrained spatial perturbations, CT intensity transformations, and stochastic multi-corruption composition to expose models to clinically plausible image degradation during training. Across two CT segmentation evaluation settings, RAMP achieved the strongest corrupted-image performance and the smallest clean-to-corrupted robustness gap. In the five-organ noisy evaluation benchmark, RAMP improved mean corrupted Dice from 0.610 to 0.753 and reduced the robustness gap from 0.264 to 0.064 compared with the nnU-Net baseline. In Abdomen1K, RAMP improved mean corrupted Dice from 0.633 to 0.789 and reduced the robustness gap from 0.290 to 0.070. Although RAMP did not achieve the highest clean-image Dice, it substantially mitigated worst-case segmentation collapse under severe image degradation. These results suggest that multi-corruption augmentation can serve as a practical pre-deployment strategy for improving the reliability of CT segmentation systems in heterogeneous clinical environments.

Problem

• CT segmentation
• robustness
• image corruption
• clinical deployment
• domain shift

Innovation

• multi-corruption augmentation
• CT segmentation robustness
• anatomically constrained perturbation
• clinical image degradation
• pre-deployment stress testing