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.

翻译：医学图像质量评估是多种医学图像分析应用中至关重要的前提条件。现有的大多数医学图像质量评估算法均为全监督方法，需要大量标注数据。然而，医学图像标注耗时耗力。本文提出了一种在训练阶段仅能获取高质量样本集的情况下，用于光学相干断层扫描血管造影图像质量评估的无监督异常感知框架，该框架结合了测试时聚类技术。具体而言，我们设计了一个基于特征嵌入的低质量表征模块，用于量化OCTA图像质量，进而区分优质图像与非优质图像。在非优质图像类别中，为了进一步区分可分级图像与不可分级图像，我们对经过训练的OCTA质量表征网络提取的多尺度图像特征进行降维和聚类。在公开数据集sOCTA-3*3-10k上进行了大量实验，结果成功验证了所提框架的优越性。