Data-Efficient Prostate Micro-Ultrasound Image Analysis using a Modality-specific Foundation Model ⋆
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Background and Objective
Micro-ultrasound (MicroUS) is an emerging high-resolution imaging modality for prostate cancer diagnosis and real-time biopsy guidance, but its automated analysis is limited by labor-intensive expert annotation. We aimed to develop and evaluate a foundation model for data-efficient MicroUS image analysis.
Methods
We developed µ FM, a MicroUS-specific foundation model pretrained using DINOv2-style self-supervised learning on 1,736,391 unlabeled images from 780 patients. The model was fine-tuned and evaluated in three downstream cohorts for prostate capsule segmentation (public, 75 patients), urethra segmentation (institutional, 105 patients), and prostate cancer lesion localization (institutional, 190 patients). Under limited annotation, µ FM was compared with randomly initialized models and foundation models pretrained on natural, medical, and ultrasound images. Evaluation used the Dice scores and 95th-percentile Hausdorff distance (HD95) for segmentation and free-response receiver operating characteristic analysis for lesion localization.
Key Findings and Limitations
µ FM improved performance across all three tasks, with the largest gains under limited annotation. With 10% of training scans, µ FM achieved Dice scores of 93.8% for prostate capsule segmentation and 58.4% for urethra segmentation, versus 87.9% and 47.0% for the strongest comparators. Corresponding HD95 values were 2.0 and 4.3 mm. For lesion localization, µ FM outperformed other models with approximately 0.75 sensitivity at two false-positive detections per 3D volume. Limitations include retrospective single-institution pretraining and lack of explicit 3D modeling.
Conclusions and Clinical Implications
MicroUS-specific self-supervised pretraining improved data-efficient image analysis across prostate and urethra segmentation, and lesion localization. This approach may reduce annotation requirements for developing automated tools in MicroUS image analysis.
Advancing Practice
What does the study add?
This study evaluates a deep learning model pretrained on a large-scale dataset of unlabeled prostate MicroUS images. The pretrained model was adapted to three clinically relevant image-analysis tasks: prostate capsule segmentation, urethra segmentation, and prostate cancer lesion localization. Substantial performance gains over other models were observed despite limited training data, suggesting that MicroUS-specific self-supervised pretraining may reduce expert annotation requirements for developing automated image-analysis tools.
Patient Summary
We developed a foundation model that learns from unlabeled prostate MicroUS images. It performed better with limited labeled data but requires independent validation before clinical use.
