前列腺特异性膜抗原（PSMA）PET成像是反映疾病阶段、治疗反应率和治疗优化选择的宝贵信息来源，尤其在PSMA放射配体治疗中。从PSMA图像中量化放射药物在健康器官中的摄取量可以通过向放射剂量递送引导个体化治疗来减少毒性。然而，器官的分割和摄取量的定量化需要繁琐的器官划定工作，在临床中通常不可行，也无法适用于大规模临床试验。在这项工作中，我们开发和测试了PSMA健康器官分割网络（PSMA-Hornet），这是一个完全自动化的深度神经网络，可以同时分割代表正常[18 F]DCFPyL PET/CT图像的14个健康器官。我们还提出了修改后的U-net架构，一种用于PET/CT图像的自监督预训练方法，多目标Dice损失和多目标批次平衡，以有效地训练PSMA-Hornet和类似的网络。该研究使用了手动分割的100个受试者的[18 F]DCFPyL PET/CT图像，以及526个没有分割的类似图像。未分割的图像用于自监督模型的预训练。对于受监督训练，采用蒙特卡罗交叉验证评估网络性能，每次试验保留85个受试者用于模型训练，5个用于验证，10个用于测试。图像分割和定量评估指标是根据核医学医师手动划分的分割结果在测试折中进行评估，并与观察者间一致性进行比较。模型的分割性能还在一组具有高肿瘤负荷的19个图像上进行了评估。在我们的最佳模型中，测试集中亚舌下腺的最低平均Dice系数为0.826，肝脏的最高为0.964。舌下腺示踪物摄取量的最大平均误差为13.9%。自监督预训练改善了训练收敛性、训练-测试泛化性和分割质量。此外，我们发现多目标网络与单器官网络相比，产生了显著更高的分割准确性。所开发的网络可以用于自动获取PSMA图像分析任务中的高质量器官分割结果。它可以用于可重复提取成像数据，并有望应用于临床实践，如个性化放射治疗剂量计算和改进的放射配体治疗。© 2023年美国医学物理学家协会。

Prostate-specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor-intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials.In this work we develop and test the PSMA Healthy organ segmentation network (PSMA-Hornet), a fully-automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18 F]DCFPyL on PET/CT images. We also propose a modified U-net architecture, a self-supervised pre-training method for PET/CT images, a multi-target Dice loss, and multi-target batch balancing to effectively train PSMA-Hornet and similar networks.The study used manually-segmented [18 F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self-supervised model pretraining. For supervised training, Monte-Carlo cross-validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter-rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load.With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self-supervised pretraining improved training convergence, train-to-test generalization, and segmentation quality. In addition, we found that a multi-target network produced significantly higher segmentation accuracy than single-organ networks.The developed network can be used to automatically obtain high-quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy.© 2023 American Association of Physicists in Medicine.