研究3D医学图像分割模型对模糊提示的鲁棒性，发现形状和空间线索的关键作用。
On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts
- 通过受控扰动模拟真实场景中的模糊提示，评估模型表现
- 发现模型对形状和空间信息依赖性强，部分扰动下仍保持稳定
- 适合关注医学图像提示分割实用性的研究者与临床应用开发者
尽管3D基础模型在可提示的医学体积分割中展现出潜力，但其对不精确提示的鲁棒性仍缺乏深入研究。本文系统地研究了多种受控扰动的密集视觉提示，这些扰动紧密模拟现实世界中的不精确性。在两个近期的基础模型上，针对多器官腹部分割任务进行实验，揭示了可提示医学分割的多个方面，尤其是对视觉形状和空间线索的依赖程度，以及模型对特定扰动的韧性。代码已公开于：https://github.com/ucsdbiag/Prompt-Robustness-MedSegFMs。
原文摘要 · Abstract (English)
While 3D foundational models have shown promise for promptable segmentation of medical volumes, their robustness to imprecise prompts remains under-explored. In this work, we aim to address this gap by systematically studying the effect of various controlled perturbations of dense visual prompts, that closely mimic real-world imprecision. By conducting experiments with two recent foundational models on a multi-organ abdominal segmentation task, we reveal several facets of promptable medical segmentation, especially pertaining to reliance on visual shape and spatial cues, and the extent of resilience of models towards certain perturbations. Codes are available at: https://github.com/ucsdbiag/Prompt-Robustness-MedSegFMs
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