Related papers: OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

URL: http://arxiv.org/abs/2409.08000v1
Date: Thu, 12 Sep 2024 12:47:34 GMT
Title: OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
Authors: Shun Zou, Zhuo Zhang, Guangwei Gao,
Abstract summary: We propose OCTAMamba, a novel U-shaped network based on the Mamba architecture to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications.
Score: 10.365417594185685
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://github.com/zs1314/OCTAMamba