Lee Ho Hin, Tang Yucheng, Bao Shunxing, Yang Qi, Xu Xin, Fogo Agnes B, Harris Raymond, de Caestecker Mark P, Spraggins Jeffrey M, Heinrich Mattias, Huo Yuankai, Landman Bennett A
Department of Computer Science, Vanderbilt University, Nashville, TN, USA 37212.
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA 37212.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12032. doi: 10.1117/12.2608290. Epub 2022 Apr 4.
The Human BioMolecular Atlas Program (HuBMAP) provides an opportunity to contextualize findings across cellular to organ systems levels. Constructing an atlas target is the primary endpoint for generalizing anatomical information across scales and populations. An initial target of HuBMAP is the kidney organ and arterial phase contrast-enhanced computed tomography (CT) provides distinctive appearance and anatomical context on the internal substructure of kidney organs such as renal context, medulla, and pelvicalyceal system. With the confounding effects of demographics and morphological characteristics of the kidney across large-scale imaging surveys, substantial variation is demonstrated with the internal substructure morphometry and the intensity contrast due to the variance of imaging protocols. Such variability increases the level of difficulty to localize the anatomical features of the kidney substructure in a well-defined spatial reference for clinical analysis. In order to stabilize the localization of kidney substructures in the context of this variability, we propose a high-resolution CT kidney substructure atlas template. Briefly, we introduce a deep learning preprocessing technique to extract the volumetric interest of the abdominal regions and further perform a deep supervised registration pipeline to stably adapt the anatomical context of the kidney internal substructure. To generate and evaluate the atlas template, arterial phase CT scans of 500 control subjects are de-identified and registered to the atlas template with a complete end-to-end pipeline. With stable registration to the abdominal wall and kidney organs, the internal substructure of both left and right kidneys are substantially localized in the high-resolution atlas space. The atlas average template successfully demonstrated the contextual details of the internal structure and was applicable to generalize the morphological variation of internal substructure across patients.
人类生物分子图谱计划（HuBMAP）提供了一个将细胞水平到器官系统水平的研究结果进行背景化的机会。构建图谱目标是跨尺度和人群概括解剖学信息的主要终点。HuBMAP的一个初始目标是肾脏器官，动脉期对比增强计算机断层扫描（CT）能为肾脏器官的内部子结构，如肾皮质、髓质和肾盂肾盏系统，提供独特的外观和解剖学背景。在大规模成像调查中，由于人口统计学因素和肾脏形态特征的混杂影响，肾脏内部子结构形态测量和强度对比因成像协议的差异而呈现出显著变化。这种变异性增加了在明确的空间参考中定位肾脏子结构解剖特征以进行临床分析的难度。为了在这种变异性情况下稳定肾脏子结构的定位，我们提出了一个高分辨率CT肾脏子结构图谱模板。简而言之，我们引入一种深度学习预处理技术来提取腹部区域的体积感兴趣区，并进一步执行深度监督配准流程，以稳定地适配肾脏内部子结构的解剖学背景。为了生成和评估图谱模板，对500名对照受试者的动脉期CT扫描进行去识别处理，并通过完整的端到端流程将其配准到图谱模板。通过与腹壁和肾脏器官的稳定配准，左右肾脏的内部子结构在高分辨率图谱空间中得到了充分定位。图谱平均模板成功展示了内部结构的背景细节，并适用于概括不同患者内部子结构的形态变化。
