Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture

Abstract

Precise delineation of the prostate gland on MRI is the cornerstone for accurate prostate cancer diagnosis, detection, characterization and treatment. The present work proposes a meta-learner deep learning (DL) network that combines the complexity of 3 well-established DL models and fine tune them in order to improve the segmentation of the prostate compared to the base learners. The backbone of the meta-learner consist the original U-net, Dense2U-net and Bridged U-net models. A model was added on top of the three base networks that has four convolutions with different receptor fields. The meta-learner outperformed the base-learners in 4 out of 5 performance metrics. The median Dice Score for the meta-learner was 89% while for the second best model it was 83%. Except for Hausdorff distance, where the meta-learner and Dense2U-net performed equally well, the improvement achieved in terms of average sensitivity, balanced accuracy, dice score and rand error, compared to the best performing base-learner, was 6%, 3%, 5% and 4%, respectively.