FAZ-BV: A Diabetic Macular Ischemia Grading Framework Combining Faz Attention Network and Blood Vessel Enhancement Filters

Monitoring the progress of diabetic macular ischemia (DMI) is essential for providing timely and effective treatment plans and prognostic evaluations. Many approaches have recently been proposed for quantifying DMI based on optical coherence tomography angiography (OCTA) images. However, none of the existing methods can effectively segment the damaged foveal avascular zone (FAZ) and blood vessels (BV) of DMI patients. To avoid this disadvantage, this study proposes a DMI grading framework, i.e. FAZ-BV, combining accurate FAZ and vessel segmentation designed for DMI. For FAZ segmentation, we propose a FAZ attention network, namely FA-Net, coupled with residual fusion attention block (RFAB). For vessel segmentation, Frangi filter and multi-scale line detector can effectively highlight vessel pixels without annotations. The selection of FAZ and BV features follows the doctors' logic of diagnosing DMI. Hybrid features are fused to build a FAZ-BV grading model for DMI. We evaluate our proposed method on a newly collected dataset with 107 eyes. Experimental results show that FA-Net surpasses state-of-the-art segmentation methods with a 95.81% Dice score, an increase of 1.61% compared with U-Net. Our framework achieves encouraging grading performance with a 0.92 AUC, which indicates that the proposed framework is of potential clinical value in DMI grading.