Wang Shuai, He Kelei, Nie Dong, Zhou Sihang, Gao Yaozong, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
Med Image Anal. 2019 May;54:168-178. doi: 10.1016/j.media.2019.03.003. Epub 2019 Mar 21.
Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intra- and inter-patient shape variability, and uncertain existence of bowel gases and fiducial markers. In this paper, we propose a novel automatic segmentation framework using fully convolutional networks with boundary sensitive representation to address this challenging problem. Our novel segmentation framework contains three modules. First, an organ localization model is designed to focus on the candidate segmentation region of each organ for better performance. Then, a boundary sensitive representation model based on multi-task learning is proposed to represent the semantic boundary information in a more robust and accurate manner. Finally, a multi-label cross-entropy loss function combining boundary sensitive representation is introduced to train a fully convolutional network for the organ segmentation. The proposed method is evaluated on a large and diverse planning CT dataset with 313 images from 313 prostate cancer patients. Experimental results show that the performance of our proposed method outperforms the baseline fully convolutional networks, as well as other state-of-the-art methods in CT male pelvic organ segmentation.
在CT图像中准确分割前列腺及危及器官（如膀胱和直肠）是前列腺癌放射治疗的关键步骤。然而，由于边界不清晰、患者内部和患者之间的形状差异大以及肠气和基准标记的存在不确定，这是一项极具挑战性的任务。在本文中，我们提出了一种新颖的自动分割框架，使用具有边界敏感表示的全卷积网络来解决这一具有挑战性的问题。我们新颖的分割框架包含三个模块。首先，设计一个器官定位模型，专注于每个器官的候选分割区域以获得更好的性能。然后，提出一种基于多任务学习的边界敏感表示模型，以更稳健和准确的方式表示语义边界信息。最后，引入一个结合边界敏感表示的多标签交叉熵损失函数来训练用于器官分割的全卷积网络。所提出的方法在一个包含来自313名前列腺癌患者的313张图像的大型多样的计划CT数据集上进行了评估。实验结果表明，我们所提出方法的性能优于基线全卷积网络以及CT男性盆腔器官分割中的其他现有最先进方法。
