School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China. Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, People's Republic of China.
Phys Med Biol. 2019 Jan 11;64(2):025015. doi: 10.1088/1361-6560/aaf83c.
The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9% ± 1.4%, 94.5% ± 2.0%, 90.6% ± 2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0% ± 10.5%, 82.2% ± 5.9% and 71.1% ± 9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation.
这项工作的目的是开发一种新颖的递归集成 OAR 分割（REOS）框架，用于准确的器官自动分割。REOS 通过集成来自器官定位模块和轮廓检测模块的图像特征来递归地分割各个 OAR。这两个模块都基于 3D U-Net 架构。器官定位模块用于粗略分割，以定位包含待描绘 OAR 的感兴趣区域（ROI），而轮廓检测模块则用于在识别的 ROI 内分割 OAR。在这项研究中，所开发的 REOS 框架应用于脑放射治疗，以分割包括眼睛、脑干（BS）、视神经和视交叉在内的六个 OAR。从 80 名脑癌患者的 80 个 T1 加权磁共振图像（MRI）中收集了带有 OAR 金标准轮廓的训练和测试 REOS。在 20 个测试案例中，REOS 实现了高分割准确性，左、右眼和 BS 的 Dice 相似系数（DSC）平均值和标准差分别为 93.9%±1.4%、94.5%±2.0%和 90.6%±2.7%。在小且分割具有挑战性的器官，即左、右眼神经和视交叉，REOS 达到了 78.0%±10.5%、82.2%±5.9%和 71.1%±9.1%的 DSC。令人满意的表现证明了 REOS 在 OAR 分割中的有效性。
