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SAM model shows stable spleen segmentation in CT scans despite domain shifts

By PulseAugur Editorial · [2 sources] ·

Researchers evaluated the robustness of the Segment Anything Model (SAM) for spleen segmentation in abdominal CT scans, simulating various domain shifts like noise and resolution changes. The study found that SAM maintained stable performance with minimal degradation in segmentation accuracy across these simulated conditions. These findings suggest SAM's potential as a reliable foundation model for medical imaging applications, including health digital twins, where consistent anatomical modeling is crucial. AI

IMPACT Suggests SAM's viability for medical imaging, potentially accelerating health digital twin development.

RANK_REASON Academic paper evaluating an existing model's performance under specific conditions.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SAM model shows stable spleen segmentation in CT scans despite domain shifts

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sanghati Basu ·

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

    arXiv:2604.25685v1 Announce Type: cross 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 insu…

  2. arXiv cs.CV TIER_1 English(EN) · Sanghati Basu ·

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

    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 sli…

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