Duanmu Hongyi, Kim Jinkoo, Kanakaraj Praitayini, Wang Andrew, Joshua John, Kong Jun, Wang Fusheng
Department of Computer Science, Stony Brook University.
Radiation Oncology, Stony Brook University Hospital.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:758-762. doi: 10.1109/isbi45749.2020.9098485. Epub 2020 May 22.
3D organ contouring is an essential step in radiation therapy treatment planning for organ dose estimation as well as for optimizing plans to reduce organs-at-risk doses. Manual contouring is time-consuming and its inter-clinician variability adversely affects the outcomes study. Such organs also vary dramatically on sizes - up to two orders of magnitude difference in volumes. In this paper, we present BrainSegNet, a novel 3D fully convolutional neural network (FCNN) based approach for automatic segmentation of brain organs. BrainSegNet takes a multiple resolution paths approach and uses a weighted loss function to solve the major challenge of the large variability in organ sizes. We evaluated our approach with a dataset of 46 Brain CT image volumes with corresponding expert organ contours as reference. Compared with those of LiviaNet and V-Net, BrainSegNet has a superior performance in segmenting tiny or thin organs, such as chiasm, optic nerves, and cochlea, and outperforms these methods in segmenting large organs as well. BrainSegNet can reduce the manual contouring time of a volume from an hour to less than two minutes, and holds high potential to improve the efficiency of radiation therapy workflow.
三维器官轮廓勾画是放射治疗治疗计划中估算器官剂量以及优化计划以降低危及器官剂量的关键步骤。手动轮廓勾画耗时且临床医生之间的差异会对结果研究产生不利影响。这些器官的大小差异也非常显著——体积相差高达两个数量级。在本文中，我们提出了BrainSegNet，这是一种基于新型三维全卷积神经网络（FCNN）的脑器官自动分割方法。BrainSegNet采用多分辨率路径方法，并使用加权损失函数来解决器官大小差异大这一主要挑战。我们使用了一个包含46个脑部CT图像体积以及相应专家器官轮廓作为参考的数据集来评估我们的方法。与LiviaNet和V-Net相比，BrainSegNet在分割微小或薄型器官（如视交叉、视神经和耳蜗）方面具有卓越性能，在分割大型器官方面也优于这些方法。BrainSegNet可以将一个体积的手动轮廓勾画时间从一小时减少到不到两分钟，并且在提高放射治疗工作流程效率方面具有很大潜力。
