Deep learning for classification of multi-sequence MR images of the prostate / 中华放射学杂志

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.

Deep learning for classification of multi?sequence MR images of the prostate / 中华放射学杂志

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.

Retrospectively analysis of the vagus nerve reflex in bronchial artery embolization / 实用放射学杂志

Objective To evaluate the cause and the treatment of the vagus nerve reflex in patients with hemoptysis during bron-chial artery embolization (BAE).Methods 1 12 patients with much hemoptysis were enrolled,9 of whom represented vagus nerve reflex in the process of interventional embolization.Results In 9 patients with mixed vagal reflex,5 occurred in the process of bron-chial artery embolization,1 in removing of sheath,1 in hemostasis by compression and 2 in returning to the ward.The intraoperative vagus reflex during BAE was related to over tension and unnormolized operation,and it improved by block of vagus nerve,raising blood pressure and fluid expansion without serious complications.Conclusion Vagus nerve reflex during BAE should be noticed, and early detection and timely intervention may improve its prognosis.