吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (2): 678-684.doi: 10.13229/j.cnki.jdxbgxb20190623

• 计算机科学与技术 • 上一篇    

基于深度学习的CT影像脑卒中精准分割

郜峰利1,2(),陶敏1,2,李雪妍1,2,何昕3,杨帆3,王卓3,宋俊峰1,2,佟丹3()   

  1. 1.吉林大学 电子科学与工程学院, 长春 130012
    2.吉林大学 集成光电子学国家重点实验室, 长春 130012
    3.吉林大学 第一医院 放射科, 长春 130021
  • 收稿日期:2019-06-20 出版日期:2020-03-01 发布日期:2020-03-08
  • 通讯作者: 佟丹 E-mail:gaofl@jlu.edu.cn;tongdan1968@126.com
  • 作者简介:郜峰利（1977-），男，教授，博士.研究方向：嵌入式系统与信号处理.E-mail: gaofl@jlu.edu.cn
  • 基金资助:
    吉林省卫生专项科研项目(2018SCZWSZX-001);吉林省自然科学基金学科布局项目(20180101038JC)

Accurate segmentation of stroke in CT image based on deep learning

Feng-li GAO1,2(),Min TAO1,2,Xue-yan LI1,2,Xin HE3,Fan YANG3,Zhuo WANG3,Jun-feng SONG1,2,Dan TONG3()   

  1. 1.College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
    2.State Key Laboratory of Integrated Optoelectronics, Jilin University, Changchun 130012, China
    3.Radiology, The First Hospital of Jilin University, Changchun 130021, China
  • Received:2019-06-20 Online:2020-03-01 Published:2020-03-08
  • Contact: Dan TONG E-mail:gaofl@jlu.edu.cn;tongdan1968@126.com

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摘要：

为解决脑卒中病变的人工定位和定量分析耗时且缺乏一致性的问题，提出了基于多尺度U-Net深度网络模型，从非增强计算机断层扫描影像中分割脑卒中病变的高密度征，同时使用Dice损失函数训练模型以对抗数据中类不平衡问题。实验数据表明:该模型可端到端的以数据驱动的方式自动学习高密度征显著特征，有效地分割脑部小病灶区域。

关键词: 影像医学与核医学, 图像分割, 脑卒中, 深度学习, 多尺度分析

Abstract:

In order to manual localization and quantitative analysis of stroke lesions is time-consuming and lacks consistency. This paper proposes a multi-scale U-Net deep network method to segment high density sign of ischemic stroke from non-enhanced Computed Tomography (CT), and use the Dice loss to train the model to combat the class imbalance in the image. Experiments show that the model can automatically learn salient features of high density sign in an end-to-end data-driven manner, effectively segmenting small lesions.

Key words: imaging and nuclear medicine, image segmentation, stroke, deep leaning, multi-scale analysis

中图分类号: 

  • TP391.4

图1

NCCT脑卒中高密度征区域标注示例"

图2

缺血性脑卒中高密度征分割模型框架"

图3

U-Net模型结构"

图4

多尺度卷积核示意图"

图5

扩张卷积示意图"

表1

U-Net网络结构参数"

层数卷积核StrideFeatures
Conv_block13×3132
Conv_block23×3164
Conv_block33×31128
Conv_block43×31256
Conv_block5*3×31512
DeConv_block13×32256
DeConv_block23×32128
DeConv_block33×3264
DeConv_block43×3232
conv1×111

表2

卷积块和反卷积块"

块定义层卷积核Stride系数
Conv_block

conv

conv

pooling

3×3

3×3

1

1

2

Dilate_Conv3×33
DeConv_block

DeConv

conv

conv

3×3

3×3

3×3

1

1

1

表3

多尺度卷积块参数"

项目卷积核StrideDilate

Multi_Conv

Pooling

Dilate_Conv

5×5

3×3

1×1

3×3

3×3

concatenate

1

1

1

2

3
DeConv

3×3

concatenate

1

表4

测试结果比较"

项目Dice
MeanSD
U-Net0.820.06
Proposed model0.850.01

图6

本文模型的训练和测试曲线"

图7

部分预测结果"

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