Multiorgan segmentation using distance-aware adversarial networks
Multiorgan segmentation using distance-aware adversarial networks
复制标题
DOI：
10.1117/1.jmi.6.1.014001
复制
发表时间：
2019-01-01
影响因子：
2.4
通讯作者：
Ruan, Su
中科院分区：
文献类型：
--
作者：
Trullo, Roger;Petitjean, Caroline;Ruan, Su
在放射治疗中，CT中危及器官的分割是非常重要的。该任务是耗时的，并且对于某些器官，由于CT中的低强度对比度，其是非常具有挑战性的。我们提出了一个框架来执行多个OAR的自动分割：食管，心脏，气管和主动脉。与以前使用深度学习技术的工作不同，我们利用全局定位信息，基于原始距离图，不仅可以定位每个器官，还可以确定它们之间的空间关系。我们不是直接分割器官，而是首先通过在对抗框架内最小化重建误差来生成定位图。然后，包括所有器官的定位信息的该图用于在完全卷积设置中指导分割任务。实验结果显示，与其他最先进的方法相比，60名患者的CT扫描共11，084个切片的性能令人鼓舞。(C)2019年，摄影光学仪器工程师协会（SPIE）
Segmentation of organs at risk (OAR) in computed tomography (CT) is of vital importance in radiotherapy treatment. This task is time consuming and for some organs, it is very challenging due to low-intensity contrast in CT. We propose a framework to perform the automatic segmentation of multiple OAR: esophagus, heart, trachea, and aorta. Different from previous works using deep learning techniques, we make use of global localization information, based on an original distance map that yields not only the localization of each organ, but also the spatial relationship between them. Instead of segmenting directly the organs, we first generate the localization map by minimizing a reconstruction error within an adversarial framework. This map that includes localization information of all organs is then used to guide the segmentation task in a fully convolutional setting. Experimental results show encouraging performance on CT scans of 60 patients totaling 11,084 slices in comparison with other state-of-the-art methods. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)