Title: Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images Show Chinese title

Title: 改进的U-Net（mU-Net）结合对象相关高级特征以提高CT图像中肝脏和肝脏肿瘤的分割效果

Abstract: Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. The proposed mU-Net outperformed existing state-of-art networks. Show Chinese abstract

Abstract: 肝脏和肝肿瘤的分割是肝细胞癌放疗中最重要的步骤之一。 该分割任务通常由人工完成，这使得过程繁琐、劳动强度大，并且容易受到操作者内部或之间的差异影响。 虽然已经提出了多种用于勾画风险器官（OARs）和肿瘤靶区的算法，但由于它们与周围器官的组织对比度较低以及在CT图像中形状可变形，肝脏和肝肿瘤的自动分割仍然难以处理。 U-Net近年来在图像分析任务中越来越受欢迎，并已显示出良好的效果。 然而，传统的U-Net架构存在三个主要缺点。 为了解决这些问题，我们在U-Net的跳跃连接中添加了一个带有转置卷积和激活操作的残差路径，以避免特征的低分辨率信息重复。 在小物体输入的情况下，跳跃连接中的特征不会与残差路径中的特征结合。 此外，所提出的架构在跳跃连接中增加了额外的卷积层，以提取小物体输入的高级全局特征以及大物体输入的高分辨率边缘信息的高级特征。 修改后的U-Net（mU-Net）的有效性通过Liver tumor segmentation（LiTS）挑战2017的公开数据集得到了验证。 所提出的mU-Net优于现有的最先进网络。