Deep learning for classification of multi-sequence MR images of the prostate
10.3760/cma.j.issn.1005-1201.2019.10.009
- VernacularTitle: 人工智能深度学习对前列腺多序列MR图像分类的可行性研究
- Author:
Junhua FANG
1
;
Qiubai LI
2
;
Chengxin YU
3
;
Xinggang WANG
4
;
Zhihua FANG
5
;
Tao LIU
4
;
Liang WANG
4
Author Information
1. Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (Now Works in Department of Radiology, Traditional Chinese Medicine Hospital of Zhijiang City, Hubei Province, Zhijiang 443200, China)
2. Department of Radiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
3. Department of Radiology, Central People′s Hospital of Yichang City, Hubei Province, Yichang 443000, China
4. Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
5. Department of Radiology, the People′s Hospital (Traditional Chinese Medicine) of Fuliang County, Jingdezhen City, Jiangxi Provicne, Jingdezhen 333000, China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Deep learning;
Prostate;
Magnetic resonance imaging;
Image classification
- From:
Chinese Journal of Radiology
2019;53(10):839-843
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a convolution neural network (CNN) model to classify multi-sequence MR images of the prostate.
Methods:ResNet18 convolution neural network (CNN) model was developed to classify multi-sequence MR images of the prostate. A deep residual network was used to improve training accuracy and test accuracy. The dataset used in this experiment included 19 146 7-sequence prostate MR images (transverse T1WI, transverse T2WI, coronal T2WI, sagittal T2WI, transverse DWI, transverse ADC, transverse PWI), from which a total of 2 800 7-sequence MR images was selected as a training set. Three hundred and eighty eight 7-sequence MR images were selected as test sets. Accuracy was used to evaluate the effectiveness of ResNet18 CNN model.
Results:The classification accuracy of the model for transverse DWI, sagittal T2WI, transverse ADC, transverse T1WI, and transverse T2WI was as high as 100.0% (44/44,52/52), and the accuracy for transverse PWI was also as high as 96.7% (116/120). The accuracy for coronal T2WI was 77.5% (31/40). 0.8% (1/120) of transverse PWI was incorrectly assigned to transverse T2WI, and 2.5% (3/120) incorrectly assigned to sagittal T2WI. 15.0% (6/40) of coronal T2WI was incorrectly assigned to transverse T2WI, and 7.5% (3/40) to sagittal T2WI.
Conclusion:The experimental results show the effectiveness of our deep learning method regarding accuracy in the prostate multi-sequence MR images detection.
