A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI.

Abstract

Objective: Prostate lesion segmentation from multiparametric MR images is particularly challenging due to the limited availability of labeled data. This scarcity of annotated images makes it difficult for supervised models to learn the complex features necessary for accurate lesion detection and segmentation. Approach: We proposed a novel semi-supervised algorithm that embeds prototype learning into mean-teacher training to improve the feature representation for unlabeled data. In this method, pseudo-labels generated by the teacher network simultaneously serve as supervision for unlabeled prototype-based segmentation. By enabling prototype segmentation to operate across labeled and unlabeled data, the network enriches the pool of "lesion representative prototypes", and allows prototypes to flow bidirectionally-from support-to-query and query-to-support paths. This intersected, bidirectional information flow strengthens the model's generalization ability. This approach is distinct from the mean-teacher algorithm as it involves few-shot training and differs from prototypical learning for adopting unlabeled data for training. Main results: This study evaluated multiple datasets with 767 patients from three different institutions, including the publicly available PROSTATEx/PROSTATEx2 datasets as the holdout institute for reproducibility. The experimental results showed that the proposed algorithm outperformed state-of-the-art semi-supervised methods with limited labeled data, observing an improvement in Dice similarity coefficient (DSC) with increasing labeled data, ranging from 0.04 to 0.09. Significance: Our method shows promise in improving segmentation outcomes with limited labeled data and potentially aiding clinicians in making informed patient treatment and management decisions.