Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Med Phys. 2023 Sep;50(9):5479-5488. doi: 10.1002/mp.16378. Epub 2023 Mar 25.
Radiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ-at-risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim to develop an MRI-based deep learning method for automatic NVB segmentation.
The proposed method, named topological modulated network, consists of three subnetworks, that is, a focal modulation, a hierarchical block and a topological fully convolutional network (FCN). The focal modulation is used to derive the location and bounds of left and right NVBs', namely the candidate volume-of-interests (VOIs). The hierarchical block aims to highlight the NVB boundaries information on derived feature map. The topological FCN then segments the NVBs inside the VOIs by considering the topological consistency nature of the vascular delineating. Based on the location information of candidate VOIs, the segmentations of NVBs can then be brought back to the input MRI's coordinate system.
A five-fold cross-validation study was performed on 60 patient cases to evaluate the performance of the proposed method. The segmented results were compared with manual contours. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD ) are (left NVB) 0.81 ± 0.10, 1.49 ± 0.88 mm, and (right NVB) 0.80 ± 0.15, 1.54 ± 1.22 mm, respectively.
We proposed a novel deep learning-based segmentation method for NVBs on pelvic MR images. The good segmentation agreement of our method with the manually drawn ground truth contours supports the feasibility of the proposed method, which can be potentially used to spare NVBs during proton and photon radiotherapy and thereby improve the quality of life for prostate cancer patients.
放射性损伤对神经血管束（NVB）的影响可能是前列腺癌放射治疗后发生性功能障碍的原因。然而，在放射治疗过程中，从计划 CT 中勾画出 NVB 作为危及器官是具有挑战性的。最近，将磁共振（MR）技术整合到放射治疗中，使得 NVB 勾画成为可能。在本研究中，我们旨在开发一种基于 MRI 的深度学习方法，用于自动 NVB 分割。
所提出的方法称为拓扑调制网络，由三个子网络组成，即焦点调制、分层块和拓扑全卷积网络（FCN）。焦点调制用于推导左右 NVB 的位置和边界，即候选感兴趣区域（VOI）。分层块旨在突出从导出特征图上 NVB 边界信息。拓扑 FCN 然后通过考虑血管描绘的拓扑一致性性质，对 VOI 内部的 NVB 进行分割。基于候选 VOI 的位置信息，可以将 NVB 的分割结果带回输入 MRI 的坐标系中。
对 60 例患者进行了五折交叉验证研究，以评估所提出方法的性能。将分割结果与手动轮廓进行比较。Dice 相似系数（DSC）和 95%的 Hausdorff 距离（HD）分别为（左 NVB）0.81±0.10，1.49±0.88mm，（右 NVB）0.80±0.15，1.54±1.22mm。
我们提出了一种基于深度学习的盆腔 MR 图像 NVB 分割新方法。该方法与手动勾画的真实轮廓具有良好的分割一致性，支持了所提出方法的可行性，该方法可用于质子和光子放射治疗中保护 NVB，从而提高前列腺癌患者的生活质量。
