Oral Presentation at the European Congress of Radiology, Vienna, 2019
Purpose
A CNN-based automatic prostate segmentation method is proposed, aiming to identify and differentiate central transitional and peripheral prostate glands as well as seminal vesicles
Methods and Materials
A total of 131 axial T2-weighted MR prostate examinations were acquired in different 3T machines and with different acquisition protocols. The central and peripheral glands and the seminal vesicles were manually labelled in all the acquired T2-weighted series by an expert to train the models. A deeply supervised U-Net based architecture was used to train this network with the Dice score coefficient as cost function and Adam as
Results
The clinical validation was performed on a different set of 25 T2-weighted cases from an external centre which was not part of the training set. The segmentation results from the network were compared and corrected by an expert radiologist to match
Conclusion
Fully automated multiregional segmentation of the prostate gland and seminal vesicles can be addressed by deeply supervised CNN. This step will help