Acquire Precise and Comparable Fundus Image Quality Score: FTHNet and FQS Dataset

TL;DR

This paper introduces a new dataset and a transformer-based model for fundus image quality assessment, achieving high correlation with expert scores and demonstrating potential for automatic medical image quality control.

Contribution

The paper presents a novel FIQA dataset with continuous and categorical labels and proposes FTHNet, a transformer-based hypernetwork for precise quality scoring of fundus images.

Findings

FTHNet achieves PLCC of 0.9423 and SRCC of 0.9488 on the FQS dataset.

The dataset includes 2246 fundus images with detailed quality labels.

FTHNet outperforms existing methods with fewer parameters.

Abstract

The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this paper, we address the limitations of datasets and algorithms in FIQA. First, we establish a new FIQA dataset, Fundus Quality Score(FQS), which includes 2246 fundus images with two labels: a continuous Mean Opinion Score varying from 0 to 100 and a three-level quality label. Then, we propose a FIQA Transformer-based Hypernetwork (FTHNet) to solve these tasks with regression results rather than classification results in conventional FIQA works. The FTHNet is optimized for the FIQA tasks with extensive experiments. Results on our FQS dataset show that the FTHNet can give quality scores for fundus images with PLCC of 0.9423 and SRCC of 0.9488, significantly outperforming other methods with fewer parameters and less computation complexity.We successfully build a dataset and model addressing the problems of current FIQA methods. Furthermore, the model deployment experiments demonstrate its potential in automatic medical image quality control. All experiments are carried out with 10-fold cross-validation to ensure the significance of the results.