Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

• URL: http://arxiv.org/abs/2604.25685v1
• Date: Tue, 28 Apr 2026 14:16:20 GMT
• Title: Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
• Abstract summary: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images.<n>We present a systematic slice-level robustness audit of SAM for spleen segmentation in abdominal CT using 1,051 nonempty slices from 41 volumes in the Medical Decathlon.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their robustness under clinically realistic medical imaging domain shifts remains insufficiently quantified. We present a systematic slice-level robustness audit of SAM (ViT-B) for spleen segmentation in abdominal CT using 1,051 nonempty slices from 41 volumes in the Medical Segmentation Decathlon. A standardized ground-truth-derived bounding-box protocol was used to isolate encoder robustness from prompt uncertainty. Controlled perturbations simulating inter-scanner variability, including Gaussian noise, blur, contrast scaling, gamma correction, and resolution mismatch, were applied across ten conditions. The clean baseline achieved a mean Dice score of 0.9145 (95% CI: [0.909, 0.919]) with a failure rate of 0.67%. Across all perturbations, the absolute mean ΔDice remained below 0.01. Paired Wilcoxon signed-rank tests with Benjamini-Hochberg false discovery rate correction identified statistically significant but small-magnitude changes under selected conditions, while McNemar analysis showed no significant increase in failure probability. These findings indicate that SAM exhibits stable segmentation behavior under moderate CT domain shifts, supporting its role as a robust foundation baseline for medical image segmentation research. As health digital twins increasingly incorporate foundation segmentation models for anatomical modeling and organ-level monitoring, formal characterization of robustness under real-world imaging variability is a necessary step toward trustworthy deployment.

Related papers

• A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation [48.14053205497089]
  MSCNet is a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions.<n>Overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W.
  arXiv  Detail & Related papers  (2026-08-17T08:11:25Z)
• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided [62.10276009853225]
  Reliable pelvic bone segmentation from CT is essential for robot-assisted pelvic trauma surgery.<n>Existing methods show limited effectiveness for PBS.<n>We propose a novel closed-loop dynamic Reliability-Guided framework (ReGA) for PBS.
  arXiv  Detail & Related papers  (2026-08-01T08:13:20Z)
• Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation [0.0]
  We present a framework for evaluating uncertainty-aware segmentation under clinical degradation.<n>We measure segmentation accuracy, calibration, failure detection, and selective prediction coverage.<n>These results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology.
  arXiv  Detail & Related papers  (2026-07-22T06:35:33Z)
• Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography [4.828654519487229]
  unsupervised anomaly detection framework learns the normative distribution of healthy retinal anatomy without lesion annotations.<n>We incorporate retinal layer-aware supervision and structured triplet learning to separate healthy from pathological representations.<n>On the Kermany dataset (AUROC: 0.799), our method substantially outperforms VAE, VQVAE, VQGAN, and f-AnoGAN baselines.
  arXiv  Detail & Related papers  (2026-04-24T01:09:05Z)
• SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation [7.174788000625294]
  Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments.<n>Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation (SHAPE) is a framework that reframes adaptation towards global anatomical plausibility.
  arXiv  Detail & Related papers  (2026-03-23T12:25:56Z)
• Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT [68.09387763135236]
  We introduce a weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks.<n>We achieve strong blastic/lytic performance despite no mask supervision.
  arXiv  Detail & Related papers  (2025-12-07T14:03:28Z)
• Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis [42.60508892284938]
  We present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging.<n>We tested 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts.<n>Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation.
  arXiv  Detail & Related papers  (2025-12-01T11:03:27Z)
• Scanner-Agnostic MRI Harmonization via SSIM-Guided Disentanglement [0.0]
  We present an image-based harmonization framework for 3D T1-weighted brain MRI.<n>The model incorporates a differentiable loss based on the Structural Similarity Index (SSIM) to preserve biologically meaningful features.<n>Visual comparisons, voxel intensity distributions, and SSIM-based metrics demonstrated that harmonized images achieved strong alignment across acquisition settings.
  arXiv  Detail & Related papers  (2025-10-24T23:19:02Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.