We introduce a novel method employing occupancy networks for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. This method considers the anatomical diversity across individuals. Our contributions include the application of occupancy networks for occluded structure localization, a robust method for estimating anatomical positions from depth images, and the creation of detailed, individualized 3D anatomical atlases. This approach promises improvements in medical imaging and automated diagnostic procedures by offering accurate, non-invasive localization of critical anatomical features.

翻译：我们提出了一种新颖方法，利用占据网络从人体外部采集的单张深度图像中精确定位67个解剖结构。该方法充分考虑了个体间的解剖学差异。我们的贡献包括：首次将占据网络应用于被遮挡结构的定位，提出从深度图像估计解剖位置的鲁棒方法，并构建了精细化的个体化三维解剖图谱。该方法通过提供关键解剖特征的精确无创定位，有望推动医学影像与自动化诊断流程的进步。