通过流体驱动的异常随机化揭示正常解剖结构
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
AI总结：
提出模态无关的UNA模型，通过流体驱动的异常随机化生成病理轮廓，实现健康脑解剖重建和异常检测，无需微调即可应用于真实病理图像。
摘要：
数据驱动的机器学习在医学图像分析领域取得了显著进展。然而，大多数现有方法针对特定模态设计，并假设特定的分辨率（通常是各向同性的）。这限制了它们在临床环境中的泛化能力，因为扫描外观的差异源于序列参数、分辨率和方向的不同。此外，大多数通用模型是为健康受试者设计的，当存在病理时会出现性能下降。我们引入了UNA（Unraveling Normal Anatomy，揭示正常解剖结构），这是首个用于正常脑解剖重建的模态无关学习方法，能够处理健康扫描和带病理的病例。我们提出了一种流体驱动的异常随机化方法，可以即时生成无限数量的逼真病理轮廓。UNA在合成和真实数据的组合上进行训练，可以直接应用于可能带有病理的真实图像，无需微调。我们展示了UNA在重建健康脑解剖方面的有效性，并展示了其直接应用于异常检测，使用来自3D健康和卒中数据集的模拟及真实图像，包括CT和MRI扫描。通过弥合健康与患病图像之间的差距，UNA使得通用模型能够用于患病图像，为在存在病理的情况下大规模分析未经整理的临床图像开辟了新的机遇。代码可在 https://github.com/peirong26/UNA 获取。
英文摘要：
Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular resolution (often isotropic). This limits their generalizability in clinical settings, where variations in scan appearance arise from differences in sequence parameters, resolution, and orientation. Furthermore, most general-purpose models are designed for healthy subjects and suffer from performance degradation when pathology is present. We introduce UNA (Unraveling Normal Anatomy), the first modality-agnostic learning approach for normal brain anatomy reconstruction that can handle both healthy scans and cases with pathology. We propose a fluid-driven anomaly randomization method that generates an unlimited number of realistic pathology profiles on-the-fly. UNA is trained on a combination of synthetic and real data, and can be applied directly to real images with potential pathology without the need for fine-tuning. We demonstrate UNA's effectiveness in reconstructing healthy brain anatomy and showcase its direct application to anomaly detection, using both simulated and real images from 3D healthy and stroke datasets, including CT and MRI scans. By bridging the gap between healthy and diseased images, UNA enables the use of general-purpose models on diseased images, opening up new opportunities for large-scale analysis of uncurated clinical images in the presence of pathology. Code is available at https://github.com/peirong26/UNA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
