Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (2): 678-684.doi: 10.13229/j.cnki.jdxbgxb20190623

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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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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

CLC Number: 

  • TP391.4

Fig.1

Radiologist manually marks stroke area"

Fig.2

Ischemic stroke segmentation model framework"

Fig.3

U-net model diagram"

Fig.4

Diagram of multi-scale convolution kernel"

Fig.5

Dilate convolution diagram"

Table 1

Parameters of U-Net in experiments"

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

Table 2

Conv_block and DeConv_ block"

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

Table 3

Parameters of multi-scale Conv_block"

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

Table 4

Testing result comparing"

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

Fig.6

Learning curves for proposed model"

Fig.7

Partial segmentation results"

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