  Our main objective is to develop a novel deep learning-based algorithm for
automatic segmentation of prostate zone and to evaluate the proposed algorithm
on an additional independent testing data in comparison with inter-reader
consistency between two experts. With IRB approval and HIPAA compliance, we
designed a novel convolutional neural network (CNN) for automatic segmentation
of the prostatic transition zone (TZ) and peripheral zone (PZ) on T2-weighted
(T2w) MRI. The total study cohort included 359 patients from two sources; 313
from a deidentified publicly available dataset (SPIE-AAPM-NCI PROSTATEX
challenge) and 46 from a large U.S. tertiary referral center with 3T MRI
(external testing dataset (ETD)). The TZ and PZ contours were manually
annotated by research fellows, supervised by genitourinary (GU) radiologists.
The model was developed using 250 patients and tested internally using the
remaining 63 patients from the PROSTATEX (internal testing dataset (ITD)) and
tested again (n=46) externally using the ETD. The Dice Similarity Coefficient
(DSC) was used to evaluate the segmentation performance. DSCs for PZ and TZ
were 0.74 and 0.86 in the ITD respectively. In the ETD, DSCs for PZ and TZ were
0.74 and 0.792, respectively. The inter-reader consistency (Expert 2 vs. Expert
1) were 0.71 (PZ) and 0.75 (TZ). This novel DL algorithm enabled automatic
segmentation of PZ and TZ with high accuracy on both ITD and ETD without a
performance difference for PZ and less than 10% TZ difference. In the ETD, the
proposed method can be comparable to experts in the segmentation of prostate
zones.
