Afmunet: Adaptive Filter-Based Frequency Modulation UNET For OCTA Segmentation

This paper presents AFMUNet, an Adaptive Filter-Based Frequency Modulation U-Net for Optical Coherence Tomography Angiography (OCTA) segmentation. The model addresses the challenge of segmenting both small blood vessels and larger vascular structures, which exhibit significant variations in scale, contrast, and connectivity. To tackle these issues, AFMUNet achieves an image-sized receptive field while capturing global dependencies, enhancing performance across diverse vessel sizes. Specifically, it incorporates the Fast Fourier Transform (FFT) applied to feature maps across multiple scales of a UNet-like architecture. This component is crucial for identifying critical global frequency patterns, enabling the model to represent the intricate details of small vessels alongside larger ones. Simultaneously, a lightweight attention block is employed to learn adaptive frequency filters from the FFT-derived representations. This mechanism selectively emphasizes transferable frequency components essential for highlighting small and thin vessels while suppressing noise and less informative features that could detract from the segmentation of large vascular structures. Experimental evaluations demonstrate that AFMUNet outperforms state-of-the-art models, achieving mean Dice and mean Intersection over Union (IoU) scores of 91.69% and 84.65%, respectively. These results underscore its robustness and superior ability to accurately segment OCTA images, effectively addressing the dual challenge of capturing both fine-grained and coarse vascular details.