Subspace-Guided Semantic and Topological Invariant Registration for Annotation-Free Ultrasound Plane Quality Control
2026-05-25 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors created STRIQ, a new method to check the quality of ultrasound images without needing special labels for each image. STRIQ uses a system that matches features between images to trusted examples found automatically in unlabeled data. They also designed a part that keeps different types of ultrasound views separate, so the quality checks don’t get mixed up. Their tests show STRIQ works well compared to clinical quality ratings, offering a way to do real-time ultrasound quality control without extra manual labeling.
ultrasound quality controlimage registrationlatent registration alignersubspace learningorthogonal knowledge subspacepseudo-labelingvariance spectrumclinical auditreal-time image analysisannotation-free learning
Authors
Chunzheng Zhu, Jianxin Lin, Feng Wang, Cheng Jiang, Guanghua Tan, Zhenyu Zhou, Shengli Li, Kenli Li
Abstract
Reliable quality control (QC) of ultrasound images is essential for both real-time acquisition guidance and retrospective clinical audit, yet existing approaches rely heavily on per-plane annotations, or employ pseudo-labeling prone to systematic bias under spatial deformations inherent in clinical acquisition. We present STRIQ, a registration-driven framework that recasts annotation-free US plane quality control as a subspace-guided consistency measurement problem. Specifically, STRIQ introduces a Latent Registration Aligner (LRA) to establish hierarchical feature space correspondences between query images and variance-driven anchors, which are autonomously distilled from unlabeled data via a variance spectrum criterion to serve as structurally stable prototypes. To further disambiguate anatomical planes and mitigate negative knowledge transfer, we propose an Orthogonal Knowledge Subspace (OKS) module. The OKS decomposes plane-specific representations into mutually orthogonal subspaces, enabling fine-grained expert collaboration while preventing inter-plane interference, ensuring that the quality metric is grounded in principled subspace proximity. Extensive experiments on the in-house US4QA and public CAMUS datasets demonstrate that STRIQ achieves state-of-the-art correlation with clinical quality scores, establishing a new paradigm for annotation-free, real-time reliable ultrasound quality control. Our code is available at https://github.com/zhcz328/STRIQ.