Dual-branch residual network for lung nodule segmentation

人工智能 计算机科学 体素 分割 模式识别(心理学) 残余物 联营 块(置换群论) 相似性(几何) 卷积神经网络 结核(地质) Sørensen–骰子系数 图像分割 计算机视觉 数学 图像(数学) 算法 古生物学 几何学 生物
作者
Haichao Cao,Feng Yu,Haichao Cao,Chih‐Cheng Hung,Guangzhi Ma,Xiangyang Xu,Renchao Jin,Jianguo Lü
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:86: 105934-105934 被引量:102
标识
DOI:10.1016/j.asoc.2019.105934
摘要

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: (1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; (2) we combine the features of the intensity and the convolutional neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC-IDRI dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with the dice similarity coefficient (DSC) of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our DSC was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.
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