Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation

2026-05-24

AI Summary

Existing self-supervised methods for ultrasound imaging often neglect anatomical context, hindering the learning of clinically aligned representations. This work proposes ANAUS, a novel framework that, for the first time, leverages anatomical structures as anchors in self-supervised learning. ANAUS introduces a learnable latent prompt engine combined with one-shot domain adaptation to enable annotation-free anatomical segmentation. It further incorporates a dual-strategy self-supervised mechanism—cross-view anatomical region semantic alignment and masked reconstruction of contextual core regions—to enhance representation invariance and fine-grained detail perception. Evaluated across six public datasets, ANAUS significantly outperforms state-of-the-art methods while maintaining computational efficiency suitable for clinical deployment.

Abstract

Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning. In this work, we propose an anatomy-anchored ultrasound self-supervision framework ANAUS that shifts representation learning from generic visual regions to clinically meaningful anatomical structures. Utilizing a learnable latent prompt engine alongside a one-time domain adaptation on existing public image--mask pairs, we empower the LP-SAM module to achieve annotation-free anatomy delineation at scale. Building upon this anatomical grounding, we propose a dual-policy self-supervised learning paradigm consisting of inter-view semantics-aware anatomy-separating alignment and contextual core-region prediction to enhance representation learning. Specifically, the former enforces feature invariance within identical anatomical regions while promoting discriminability across distinct structures; the latter compels the model to reconstruct corrupted regions, thereby capturing fine-grained structural details. Extensive evaluations on six public datasets demonstrate that \ours{} consistently outstrips current state-of-the-art methods while maintaining the computational efficiency essential for clinical deployment. Code is available at https://github.com/zhcz328/ANAUS.

Problem

• ultrasound
• self-supervised learning
• anatomical context
• representation learning
• medical imaging

Innovation

• anatomy-anchored
• self-supervised learning
• ultrasound representation
• LP-SAM
• dual-policy alignment