Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation

2026-08-14

AI Summary

This study addresses the challenges of speckle noise interference and high annotation costs in high-frequency micro-ultrasound prostate segmentation by proposing the first zero-shot segmentation framework. Integrating the MedSAM foundation model with CLAHE enhancement and Fourier smoothing, this approach validates the efficacy of box prompts while demonstrating the infeasibility of point prompts, achieving high-precision segmentation without training data. Experimental results indicate a Dice coefficient of 0.865 and a 45% reduction in boundary error, with consistency surpassing non-expert annotations. By eliminating the dependency on large-scale annotated datasets for this modality, this work establishes an efficient paradigm for precise clinical diagnosis and treatment.

Abstract

Prostate cancer claims a life every 80 seconds. Early detection is needed to prevent disease progression, and both PSA density calculation and biopsy decisions rely on knowing the exact boundary of the gland. Conventional ultrasound at 6-12 MHz blurs this boundary, missing one in three high-risk cancers. Micro-ultrasound (29 MHz) improves resolution threefold but introduces dense acoustic speckle that obscures the outer wall; given the same image, two clinicians draw outlines differing by over 10% in area. Supervised methods are costly and generalise poorly across scanners. Can a foundation model segment the prostate with no training data? We present the first zero-shot pipeline for this modality: MedSAM, pre-trained on over 1.5 million medical images, localises the prostate; we then apply CLAHE to sharpen the outer wall, binary dilation to recover missed pixels, and Fourier smoothing (4 modes, s=1.05) to refine the boundary. MedSAM requires a spatial prompt, so we evaluate bounding-box and point-click strategies across 75 patients of the Micro-Ultrasound Prostate Segmentation dataset (2,621 slices). On the 20-patient held-out test set, the pipeline reduces mean boundary-distance error by 45% (Dice 0.749+/-0.043 to 0.865+/-0.029; HD95 217.2+/-36.9 to 120.1+/-26.1 px), reaching Dice 0.859 across the cohort. Its mean overlap shows no significant difference from the three non-expert rater groups (p>0.19), while segmenting 38-52% more consistently (lower inter-patient standard deviation). Point-click prompts fail regardless of placement (best Dice=0.350), because speckle gives no stable local contrast. Only an approximate bounding box is required, so any clinic can deploy it without data collection, annotation, or retraining.

Problem

• Micro-Ultrasound
• Prostate Segmentation
• Zero-Shot Adaptation
• Medical Vision Foundation Models

Innovation

• Zero-Shot Adaptation
• Medical Vision Foundation Model
• Micro-Ultrasound
• Prostate Segmentation
• MedSAM