重新思考医学图像翻译的感知度量
Rethinking Perceptual Metrics for Medical Image Translation
AI总结：
该研究针对医学图像翻译的评估问题，测试了多种感知度量在两项翻译任务上的表现，发现常用感知度量与分割任务相关性差，而像素级SWD度量或适用于细微模态内翻译，指出需进一步研究适配医学场景的评估度量。
摘要：
现代医学图像翻译方法使用生成模型完成CT图像转MRI等任务。评估这些方法通常依赖目标域中选定的下游任务，例如分割。另一方面，与任务无关的度量颇具吸引力，比如通用计算机视觉领域图像翻译中常用的基于网络特征的感知度量（例如FID）。本文在两项医学图像翻译任务（GE乳腺MRI转Siemens乳腺MRI、腰椎MRI转CT）上研究医学图像翻译的评估度量，对多种前沿翻译方法进行了测试。研究表明，感知度量通常与分割度量不相关，因为它们难以适配该子领域的解剖学约束，其中FID的不一致性尤为突出。不过，我们发现较少使用的像素级SWD度量可能对细微的模态内翻译有用。研究结果表明，需要进一步研究适用于医学图像翻译的有效度量。
英文摘要：
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI. Evaluating these methods typically relies on some chosen downstream task in the target domain, such as segmentation. On the other hand, task-agnostic metrics are attractive, such as the network feature-based perceptual metrics (e.g., FID) that are common to image translation in general computer vision. In this paper, we investigate evaluation metrics for medical image translation on two medical image translation tasks (GE breast MRI to Siemens breast MRI and lumbar spine MRI to CT), tested on various state-of-the-art translation methods. We show that perceptual metrics do not generally correlate with segmentation metrics due to them extending poorly to the anatomical constraints of this sub-field, with FID being especially inconsistent. However, we find that the lesser-used pixel-level SWD metric may be useful for subtle intra-modality translation. Our results demonstrate the need for further research into helpful metrics for medical image translation.
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