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

TL;DR

This paper introduces an unsupervised framework for OCTA image quality assessment that leverages test-time clustering and anomaly detection, reducing the need for extensive annotated data.

Contribution

It proposes a novel unsupervised anomaly-aware approach with test-time clustering for OCTA image quality assessment, effective with limited training data.

Findings

Outperforms existing methods on the sOCTA-3*3-10k dataset.

Effectively distinguishes high-quality, gradable, and ungradable images.

Requires only high-quality samples for training.

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.