Deep learning hybrid 3D/2D convolutional neural network for prostate MRI recognition.

Abstract

e16600 Background: Prostate cancer is the most common cancer of men in the United States, with over 200,000 new cases diagnosed in 2018. Multiparametric MRI of the prostate (mpMRI) has emerged as valuable adjunct for the detection and characterization of prostate cancer as well as for guidance of prostate biopsy. As mpMRI progresses towards widespread clinical use, major challenges have been identified, arising from the need to increase accuracy of mpMRI localization of prostate lesions, improve in lesion categorization, and decrease the time and technical complexity of mpMRI evaluation by radiologists or urologists. Deep learning convolutional neural networks (CNN) for image recognition are becoming a more common method of machine learning and show promise in evaluation of complex medical imaging. In this study we describe a deep learning approach for automatic localization and segmentation of prostates organ on clinically acquired mpMRIs. Methods: This IRB approved retrospective review included patients who had a prostate MRI between September 2014 and August 2018 and an MR-guided transrectal biopsy. For each mpMRI the prostate was manually segmented by a board-certified abdominal radiologist on T2 weighted sequence. A hybrid 3D/2D CNN based on U-Net architecture was developed and trained using these manually segmented images to perform automated organ segmentation. After training, the CNN was used to produce prostate segmentations autonomously on clinical mpMRI. Accuracy of the CNN was assessed by Sørensen–Dice coefficient and Pearson coefficient. Five-fold validation was performed. Results: The CNN was successfully trained and five-fold validation performed on 411 prostate mpMRIs. The Sørensen–Dice coefficient from the five-fold cross validation was 0.87 and the Pearson correlation coefficient for segmented volume was 0.99. Conclusions: These results demonstrate that a CNN can be developed and trained to automatically localize and volumetrically segment the prostate on clinical mpMRI with high accuracy. This study supports the potential for developing an automated deep learning CNN for organ segmentation to replace clinical manual segmentation. Future studies will look towards prostate lesion localization and categorization on mpMRI.