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
;
Chengxin YU
;
Xinggang WANG
;
Zhihua FANG
;
Tao LIU
;
Liang WANG
Author Information
1. 华中科技大学同济医学院附属同济医院放射科
- 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.
