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 optimization algorithm. To maximize the performance of the CNN, a Cyclic Learning Rate was used during the training stage. Also, Image Processing algorithms were used to further refine the predicted segmentation masks during inference.

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 best truth. Finally, the Dice score coefficient between the model’s predictions and the expert corrected masks was calculated. The scores for the central-transitional gland, peripheral gland, seminal vesicles and background were 0.92±0.03, 0.90±0.05, 0.91±0.05, and 0.99±0.00, respectively.

Conclusion

Fully automated multiregional segmentation of the prostate gland and seminal vesicles can be addressed by deeply supervised CNN. This step will help localizing prostatic lesions and characterizing the pattern of prostatic enlargement.