健康胰腺的多对比计算机断层扫描图谱
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
AI总结：
本文提出面向多对比CT的高分辨率健康胰腺图谱框架，结合深度学习预处理与DEEDs分层配准，并在443名受试者及100例外部队列上验证其标签转移与泛化性能。
摘要：
由于人口统计学特征存在显著差异，例如年龄和身体成分的不同，胰腺的体积形态差异很大，从而导致形状和外观出现明显变化。这些变化增加了推广全人群胰腺特征的难度。需要一个体积空间参考，以适应器官特异性分析中的形态变异性。在此，我们提出了一种高分辨率计算机断层扫描（CT）图谱框架，专门针对多对比CT中的胰腺器官进行优化。我们引入了一种基于深度学习的预处理技术来提取腹部感兴趣区域（ROIs），并利用分层配准流程来对齐不同人群的胰腺解剖结构。简而言之，执行DEEDs仿射和非刚性配准，将患者腹部体积转移到固定的高分辨率图谱模板。为了生成和评估胰腺图谱模板，我们处理了443名受试者的多对比模态CT扫描（无报告胰腺疾病史，年龄：15-50岁）。与不同的最先进配准工具相比，DEEDs仿射和非刚性配准的组合在所有对比期相中均实现了最佳的胰腺标签转移性能。我们进一步使用另一个研究队列进行外部评估，该队列包含100例去标识化的门静脉期扫描，并标注了13个器官；在无监督设置下，其取得了0.504 Dice分数的最佳标签转移性能。各期相的定性表示（例如平均映射）形成了清晰的胰腺边界及其独特的对比外观。不同尺度（例如从小到大的体积）的变形表面渲染进一步说明了所提出图谱模板的泛化能力。
英文摘要：
With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. Here, we proposed a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ across multi-contrast CT. We introduce a deep learning-based pre-processing technique to extract the abdominal region of interests (ROIs) and leverage a hierarchical registration pipeline to align the pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template. To generate and evaluate the pancreas atlas template, multi-contrast modality CT scans of 443 subjects (without reported history of pancreatic disease, age: 15-50 years old) are processed. Comparing with different registration state-of-the-art tools, the combination of DEEDs affine and non-rigid registration achieves the best performance for the pancreas label transfer across all contrast phases. We further perform external evaluation with another research cohort of 100 de-identified portal venous scans with 13 organs labeled, having the best label transfer performance of 0.504 Dice score in unsupervised setting. The qualitative representation (e.g., average mapping) of each phase creates a clear boundary of pancreas and its distinctive contrast appearance. The deformation surface renderings across scales (e.g., small to large volume) further illustrate the generalizability of the proposed atlas template.
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