UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment

• URL: http://arxiv.org/abs/2212.10541v1
• Date: Tue, 20 Dec 2022 18:48:04 GMT
• Title: UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
• Authors: Juntao Chen, Li Lin, Pujin Cheng, Yijin Huang, Xiaoying Tang
• Abstract summary: We propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography ( OCTA) image quality assessment. A feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images. We perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network.
• Score: 4.901218498977952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications. Most existing MIQA algorithms are fully supervised that request a large amount of annotated data. However, annotating medical images is time-consuming and labor-intensive. In this paper, we propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography (OCTA) image quality assessment in a setting wherein only a set of high-quality samples are accessible in the training phase. Specifically, a feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images and then to discriminate between outstanding quality and non-outstanding quality. Within the non-outstanding quality class, to further distinguish gradable images from ungradable ones, we perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network. Extensive experiments are conducted on one publicly accessible dataset sOCTA-3*3-10k, with superiority of our proposed framework being successfully established.

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