Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Med Phys. 2019 Jul;46(7):3133-3141. doi: 10.1002/mp.13560. Epub 2019 May 21.
Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and subject to inter- and intraobserver variation. To address these drawbacks, we proposed a deep learning-based method to automatically segment AVMs on CT simulation image sets.
We developed a deep learning-based method using a deeply supervised three-dimensional (3D) V-Net with a compound loss function. A 3D supervision mechanism was integrated into a residual network, V-Net, to deal with the optimization difficulties when training deep networks with limited training data. The proposed compound loss function including logistic and Dice losses encouraged similarity and penalized discrepancy simultaneously between prediction and training dataset; this was utilized to supervise the 3D V-Net at different stages. To evaluate the accuracy of segmentation, we retrospectively investigated 80 AVM patients who had CT simulation and digital subtraction angiography (DSA) acquired prior to treatment. The AVM target volume was segmented by our proposed method. They were compared with clinical contours approved by physicians with regard to Dice overlapping, difference in volume and centroid, and dose coverage changes on original plan.
Contours created by the proposed method demonstrated very good visual agreement to the ground truth contours. The mean Dice similarity coefficient (DSC), sensitivity and specificity of the contours delineated by our method were >0.85 among all patients. The mean centroid distance between our results and ground truth was 0.675 ± 0.401 mm, and was not significantly different in any of the three orthogonal directions. The correlation coefficient between ground truth and AVM volume resulting from the proposed method was 0.992 with statistical significance. The mean volume difference among all patients was 0.076 ± 0.728 cc; there was no statistically significant difference. The average differences in dose metrics were all less than 0.2 Gy, with standard deviation less than 1 Gy. No statistically significant differences were observed in any of the dose metrics.
We developed a novel, deeply supervised, deep learning-based approach to automatically segment the AVM volume on CT images. We demonstrated its clinical feasibility by validating the shape and positional accuracy, and dose coverage of the automatic volume. These results demonstrate the potential of a learning-based segmentation method for delineating AVMs in the clinical setting.
立体定向放射外科（SRS）广泛用于破坏动静脉畸形（AVM）。其性能依赖于精确勾画目标 AVM。在框架 SRS 手术期间手动分割既耗时又存在观察者内和观察者间的差异。为了解决这些缺点，我们提出了一种基于深度学习的方法，用于自动分割 CT 模拟图像集上的 AVM。
我们开发了一种基于深度学习的方法，使用带有复合损失函数的深度监督三维（3D）V-Net。将 3D 监督机制集成到残差网络 V-Net 中，以解决在有限训练数据下训练深度网络时的优化难题。所提出的复合损失函数包括逻辑和 Dice 损失，鼓励预测和训练数据集之间的相似性和差异，同时在不同阶段对 3D V-Net 进行监督。为了评估分割的准确性，我们回顾性地研究了 80 名接受治疗前 CT 模拟和数字减影血管造影（DSA）检查的 AVM 患者。通过我们提出的方法对 AVM 靶体积进行分割。将其与经医生批准的临床轮廓进行比较，比较内容包括 Dice 重叠率、体积和质心差异以及原始计划中的剂量覆盖变化。
所提出的方法生成的轮廓与真实轮廓具有非常好的视觉一致性。在所有患者中，所提出的方法勾画的轮廓的平均 Dice 相似系数（DSC）、敏感度和特异性均>0.85。我们的结果与真实值之间的平均质心距离为 0.675±0.401mm，在三个正交方向上均无显著差异。所提出方法得到的真实值与 AVM 体积之间的相关系数为 0.992，具有统计学意义。所有患者的平均体积差异为 0.076±0.728cc，无统计学差异。剂量指标的平均差异均小于 0.2Gy，标准差小于 1Gy。在任何剂量指标上均未观察到统计学差异。
我们开发了一种新的、深度监督的、基于深度学习的方法，用于自动分割 CT 图像上的 AVM 体积。通过验证自动体积的形状和位置准确性以及剂量覆盖，我们证明了其临床可行性。这些结果表明，基于学习的分割方法在临床环境中勾画 AVM 具有潜力。
