Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is
an essential part of planning effective treatment strategies to combat lung and
esophageal cancer. Accurate segmentation of organs surrounding tumours helps
account for the variation in position and morphology inherent across patients,
thereby facilitating adaptive and computer-assisted radiotherapy. Although
manual delineation of OARs is still highly prevalent, it is prone to errors due
to complex variations in the shape and position of organs across patients, and
low soft tissue contrast between neighbouring organs in CT images. Recently,
deep convolutional neural networks (CNNs) have gained tremendous traction and
achieved state-of-the-art results in medical image segmentation. In this paper,
we propose a deep learning framework to segment OARs in thoracic CT images,
specifically for the: heart, esophagus, trachea and aorta. Our approach employs
dilated convolutions and aggregated residual connections in the bottleneck of a
U-Net styled network, which incorporates global context and dense information.
Our method achieved an overall Dice score of 91.57% on 20 unseen test samples
from the ISBI 2019 SegTHOR challenge.

本研究提出了一种深度学习框架，用于在胸部 CT 图像中分割器官的关键部分，包括心脏、食管、气管和主动脉，该方法采用扩张卷积和聚合残差连接，在网络的瓶颈处融入全局上下文和密集信息的 U-Net 风格网络，取得了 ISBI 2019 SegTHOR 挑战赛上 20 张未见过的测试样本中总的 Dice 得分为 91.57% 的优异表现。