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SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS

机译:锋利的构架和条件随机场将胸椎CT图像中的有机物区分开

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摘要

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.
机译:癌症是全球死亡的主要原因之一。放射治疗是针对这种情况的标准治疗方法,放射治疗过程的第一步是确定要靶向的目标量和要保护的健康风险器官(OAR)。与以前的OAR自动分割方法通常使用本地信息并分别对每个OAR进行分割不同,在本文中,我们提出了一种深度学习框架,用于在胸部CT图像(特别是心脏,食道,气管和CT)中对OAR进行联合分割。主动脉。利用完全卷积网络(FCN),我们提出了几种改进性能的扩展,包括允许使用低级功能和高级信息的新体系结构,有效地结合了本地和全局信息以提高定位精度。最后,通过使用条件随机场(特别是作为递归神经网络模型的CRF),我们能够考虑器官之间的关系以进一步改善分割结果。实验证明,在30个CT扫描的数据集上具有竞争优势。

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