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一种基于级联卷积网络的三维脑肿瘤精细分割

Fine-Granted Segmentation Method for Three-Dimensional Brain Tumors Using Cascaded Convolutional Network

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摘要

提出了一种基于级联的2.5维(2.5D)卷积神经网络。将该任务拆分为脑肿瘤整体分割、肿瘤核分割、增强肿瘤分割三个子任务,并将三个结果合并生成最终结果。在每个子任务中,对三维(3D)图像进行轴向、矢向和冠向的裁取,生成2.5D图像;将2.5D图像输入至所提2.5D V-Net中进行训练;将2.5D分割结果拼接成3D结果,生成不同子任务的分割结果图。结果表明,所提方法对肿瘤整体、肿瘤核和增强肿瘤分割的平均dice值分别可达0.9071、0.8542和0.8140,基本满足临床需要。

Abstract

A cascaded 2.5-dimensional (2.5D) convolutional neural network is proposed. The task is divided into three sub-tasks of whole tumor segmentation, tumor core segmentation and enhancing tumor segmentation, and the results are combined to generate the final result. In each sub-task, the three-dimensional (3D) images are horizontally, coronally and sagittally cropped to generate 2.5D images. The 2.5D images are fed into the proposed 2.5D V-Net for training. The 2.5D segmentation results are concatenated as the 3D results to generate the segmentation results of different sub-tasks. The results show that the average dice scores for the segmentation of whole tumor, tumor core and enhancing tumor by the proposed method are 0.9071, 0.8542, and 0.8140, respectively, which basically meet the clinic need.

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中图分类号:TP391.4

DOI:10.3788/lop56.101001

所属栏目:图像处理

基金项目:国家自然科学基金(61271069)

收稿日期:2018-09-25

修改稿日期:2018-12-03

网络出版日期:2018-12-13

作者单位    点击查看

褚晶辉:天津大学电气自动化与信息工程学院, 天津 300072
李晓川:天津大学电气自动化与信息工程学院, 天津 300072
张佳祺:天津大学电气自动化与信息工程学院, 天津 300072
吕卫:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:吕卫(luwei@tju.edu.cn)

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引用该论文

Chu Jinghui,Li Xiaochuan,Zhang Jiaqi,Lü Wei. Fine-Granted Segmentation Method for Three-Dimensional Brain Tumors Using Cascaded Convolutional Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101001

褚晶辉,李晓川,张佳祺,吕卫. 一种基于级联卷积网络的三维脑肿瘤精细分割[J]. 激光与光电子学进展, 2019, 56(10): 101001

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