Semi-Supervised Learning in Prostate MRI Tumor Segmentation Approaches Fully-Supervised Performance on External Validation

Purpose To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer segmentation on MRI compared to fully-supervised models trained with additional expert annotations. Materials and Methods We used 1500 MRI scans from the PI-CAI challenge training subset. Positive scans had 220 human and 205 AI-generated annotations. The mtU-Net (proposed teacher-student semi-supervised approach) was compared to supervised (trained using only 220 human annotations) and semi-supervised (trained on human and AI-generated annotations) nnU-Net. The 205 AI-annotated scans were manually annotated, and a fully-supervised model was trained. External validation was performed on a newly annotated dataset from the PROMIS study (n=574) and the Prostate158 dataset (n=158). Patient-level performance was evaluated using Area Under the Curve (AUC), Average Precision (AP) for lesion-level detection, and the DeLong test to compare performance. Results The fully-supervised nnU-Net showed the highest performance on the internal PI-CAI test set (AUC=0.89[0.87-0.91]/AP=0.65[0.60-0.70]) and external validation datasets PROMIS (AUC=0.70[0.66-0.74]/AP=0.24[0.19-0.29]) and Prostate158 (AUC=0.87[0.82-0.92]/AP=0.64[0.56-0.72]), significantly outperforming the supervised baseline (p≤0.002). The proposed semi-supervised mtU-Net demonstrated close external validation performance on PROMIS (AUC=0.66[0.62-0.71]/AP=0.20[0.16-0.25]) and Prostate158 (AUC=0.86[0.81-0.92]/AP=0.58[0.49-0.67]), significantly outperforming the supervised baseline on both datasets (p=0.024 and p=0.007, respectively). Semi-supervised nnU-Net showed intermediate results on PROMIS (AUC=0.65[0.60-0.69]/AP=0.20[0.16-0.24]) and Prostate158 (AUC=0.81[0.74-0.88]/AP=0.53[0.44-0.62]), significantly outperforming the supervised baseline only on PROMIS (p=0.042). Conclusion In prostate MRI tumor segmentation, nnU-Net fully-supervised learning performed best. However, in external validation, mtU-Net’s semi-supervised learning performance approached the fully-supervised model, demonstrating a valuable approach when expert annotations are limited. Summary Semi-supervised learning achieves close performance to fully-supervised methods on external validation in prostate cancer segmentation, reducing dependence on expert annotations in increasing demands. Key points The inclusion of AI-annotated data during training showed close performance to annotating additional samples with expert delineations, suggesting data diversity may be as impactful as increased expert annotation volume. The combination of pseudo-labeling with consistency regularization within the semi-supervised mtU-Net framework mitigated the impact of potential inaccuracies in AI-generated annotations, resulting in performance approaching that of fully-supervised models.