多模态交互式肺病变分割：基于生理和解剖学线索的PET/CT图像标注框架
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
AI总结：
提出一种结合PET/CT解剖与生理线索的多模态交互式肺病变分割框架，利用测地距离变换和椭球体用户模拟方案减少标注成本，并在多数据集上验证了有效性。
摘要：
近年来，深度学习实现了医学图像中各种疾病的精准分割。然而，这些性能通常需要大量手动体素标注。对于体积数据，这一繁琐过程在所需信息并非全部存在于单一成像域（如PET/CT数据）时会变得更加复杂。我们提出了一种多模态交互式分割框架，通过结合PET/CT数据中的解剖学和生理学线索来缓解这些问题。该框架利用测地距离变换表示用户标注，并在训练期间实现了一种新颖的基于椭球体的用户模拟方案。我们进一步提出了两种标注界面，并开展用户研究以评估其可用性。我们在域内验证数据集和未见过的PET/CT数据集上评估了模型。代码已公开：https://github.com/verena-hallitschke/pet-ct-annotate。
英文摘要：
Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging. These performances, however, typically demand large amounts of manual voxel annotations. This tedious process for volumetric data becomes more complex when not all required information is available in a single imaging domain as is the case for PET/CT data. We propose a multimodal interactive segmentation framework that mitigates these issues by combining anatomical and physiological cues from PET/CT data. Our framework utilizes the geodesic distance transform to represent the user annotations and we implement a novel ellipsoid-based user simulation scheme during training. We further propose two annotation interfaces and conduct a user study to estimate their usability. We evaluated our model on the in-domain validation dataset and an unseen PET/CT dataset. We make our code publicly available: https://github.com/verena-hallitschke/pet-ct-annotate.
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