Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs‐at‐risk, which is laborious and time‐consuming. We present a fully automated segmentation method based on the three‐dimensional (3D) U‐Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs‐at‐risk to all tissues within the abdomen.

中文翻译：

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使用U-Net变体架构完成腹部和骨盆分割。

计算机断层扫描（CT）成像的器官分割对于放疗治疗计划至关重要。治疗计划不仅需要对受影响的组织进行分割，而且还需要对附近处于危险状态的健康器官进行分割，这既费力又费时。我们提出了一种基于三维（3D）U-Net卷积神经网络（CNN）的全自动分割方法，该方法能够将整个腹部和骨盆分割为33种独特的器官和组织结构，包括可能被其他自动分割忽略的组织诸如脂肪组织，骨骼肌以及结缔组织和血管的治疗方法。整个腹部的分割能够量化少数处于危险中的器官对腹部所有组织的暴露。