  Prostate cancer (PCa) is one of the most common cancers in men around the
world. The most accurate method to evaluate lesion levels of PCa is microscopic
inspection of stained biopsy tissue and estimate the Gleason score of tissue
microarray (TMA) image by expert pathologists. However, it is time-consuming
for pathologists to identify the cellular and glandular patterns for Gleason
grading in large TMA images. We used Gleason2019 Challenge dataset to build a
convolutional neural network (CNN) model to segment TMA images to regions of
different Gleason grades and predict the Gleason score according to the grading
segmentation. We used a pre-trained model of prostate segmentation to increase
the accuracy of the Gleason grade segmentation. The model achieved a mean Dice
of 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with a
score of 0.778 combined of Cohen's kappa and the f1-score.
