Data_Sheet_1_Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy.docx

BackgroundConvolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and—like a human observer—may be trained based on a relatively small cohort of reference subjects. Rapid, accurate organ recognition in medical imaging permits a variety of new quantitative diagnostic techniques. In the case of therapy with targeted radionuclides, it may permit comprehensive radiation dose analysis in a manner that would often be prohibitively time-consuming using conventional methods.MethodsAn automated image segmentation tool was developed based on three-dimensional CNNs to detect right and left kidney contours on non-contrast CT images. Model was trained based on 89 manually contoured cases and tested on a cohort of patients receiving therapy with 177Lu-prostate-specific membrane antigen-617 for metastatic prostate cancer. Automatically generated contours were compared with those drawn by an expert and assessed for similarity based on dice score, mean distance-to-agreement, and total segmented volume. Further, the contours were applied to voxel dose maps computed from post-treatment quantitative SPECT imaging to estimate renal radiation dose from therapy.ResultsNeural network segmentation was able to identify right and left kidneys in all patients with a high degree of accuracy. The system was integrated into the hospital image database, returning contours for a selected study in approximately 90 s. Mean dice score was 0.91 and 0.86 for right and left kidneys, respectively. Poor performance was observed in three patients with cystic kidneys of which only few were included in the training data. No significant difference in mean radiation absorbed dose was observed between the manual and automated algorithms.ConclusionAutomated contouring using CNNs shows promise in providing quantitative assessment of functional SPECT and possibly PET images; in this case demonstrating comparable accuracy for radiation dose interpretation in unsealed source therapy relative to a human observer.

背景卷积神经网络（CNNs）已被证实是辅助物体检测的强大工具，其训练过程类似于人类观察者，仅需基于相对较小的参考受试者群体即可进行。在医学影像中快速、准确地识别器官，允许采用各种新的定量诊断技术。在针对放射性核素的治疗中，它可能允许以传统方法通常难以承受的时间成本进行全面的辐射剂量分析。方法：基于三维CNN开发了一种自动图像分割工具，用于在非对比CT图像上检测左右肾脏轮廓。模型基于89例手动勾勒的病例进行训练，并在接受177Lu-前列腺特异性膜抗原-617治疗转移性前列腺癌的患者群体上进行测试。自动生成的轮廓与专家绘制的轮廓进行比较，并基于dice分数、平均距离一致性以及总分割体积进行相似性评估。此外，轮廓被应用于从治疗后定量SPECT成像计算出的体素剂量图中，以估算肾脏的辐射剂量。结果：神经网络分割能够以高精度识别所有患者的左右肾脏。该系统已集成到医院图像数据库中，对所选研究进行轮廓返回的时间约为90秒。左右肾脏的平均dice分数分别为0.91和0.86。在训练数据中包含少量病例的三个囊性肾脏患者中，观察到性能较差。手动和自动算法之间的平均辐射吸收剂量没有观察到显著差异。结论：使用CNNs进行的自动轮廓绘制在提供功能SPECT的定量评估方面显示出前景；在本例中，与人类观察者相比，在未密封源治疗中，辐射剂量解释的准确性相当。