Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Materials and methods: 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: The DSC, ASD, and HD95 values for the test dataset were 62.2%, 2.54mm, and 7mm, respectively. AI segmented contours were dosimetrically equivalent to the expert physician's contours. The observer study showed that expert physicians' scored AI contours (mean=3.7) higher than inexperienced physicians' contours (mean=3.1). When inexperienced physicians started with AI contours, the score improved to 3.7. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

翻译：背景与目的：放射诱导的勃起功能障碍（RiED）常见于前列腺癌患者。多家机构已开展临床试验，探究减少阴部内动脉（IPA）照射剂量能否改善性功能保留。由于分割难度，IPA通常不被视为常规危及器官（OAR）。本研究提出一种基于深度学习（DL）的IPA自动分割模型，该模型可采用CT联合MRI或仅CT作为输入图像模态，以适应临床实践差异。材料与方法：本研究纳入86例具有CT和MRI图像及含噪声IPA标签的患者。将数据按42/14/30分别划分为模型训练集、测试集与临床观察研究集。模型包含三项主要创新：1）设计包含压缩激励模块与模态注意力机制的网络架构，实现有效特征提取与精确分割；2）采用新型损失函数训练含噪声标签的模型；3）引入模态丢弃策略，使模型具备无MRI条件下的分割能力。结果：测试数据集的Dice相似系数（DSC）、平均表面距离（ASD）与Hausdorff距离95%（HD95）分别为62.2%、2.54mm与7mm。AI分割轮廓在剂量学上与专家医师轮廓等效。观察研究表明，专家医师对AI轮廓的评分（均值=3.7）高于经验不足医师的轮廓（均值=3.1）。当经验不足医师以AI轮廓为起点时，评分提升至3.7。结论：本模型生成的高质量IPA轮廓可提升分割一致性，推动标准化IPA分割进入临床试验与实践。