Work

LHU-VT: A Lightweight Hypercomplex U-Net with Vessel Thickness-Guided Dice Loss for retinal vessel segmentation

Year: 2024

Type: article

Abstract: Vision loss is often caused by retinal disorders, such as age-related macular degeneration and diabetic retinopathy, where early indicators like microaneurysms and hemorrhages appear as changes in retinal blood vessels. Accurate segmentation of these vessels in retinal images is essential for early diagnosis. However, retinal vessel segmentation presents challenges due to complex vessel structures, low contrast, and dense branching patterns, which are further complicated in resource-limited settings requiring lightweight solutions. To address these challenges, we propose a novel Lightweight Hypercomplex U-Net 1 1 A symmetric U-shaped convolutional encoder/decoder structure with contracting and expanding paths, designed for image segmentation. (LHUN) with Vessel Thickness-Guided Dice Loss (VTDL), collectively called LHU-VT. LHUN utilizes hypercomplex octonions to capture intricate patterns and cross-channel relationships in fundus images, reducing parameter count and enabling edge deployment. The VTDL component applies vessel thickness-guided weights to address class imbalance, thereby enhancing segmentation accuracy. Our experiments show that LHU-VT significantly outperforms current methods, achieving up to 2.4 × fewer FLOPs, 4.4 × fewer parameters, and 2.6 × smaller model size. The model achieves AUC scores of 0.9938, 0.9879, 0.9988, and 0.9808, respectively, on four benchmark datasets CHASE, DRIVE, STARE, and HRF. • We propose a lightweight Hypercomplex U-Net designed for retinal vessel segmentation. • Hypercomplex number system to extract complex inter-channel relationships within data. • Pixel-wise vessel thickness-guided weighted dice loss to tackle the class imbalance. • Achieved reductions of 2.4 × in FLOPs, 4.4 × in parameters, and 2.6 × in model size. • Achieved AUCs of 0.9938, 0.9879, 0.9988, and 0.9808 on CHASE, DRIVE, STARE, and HRF.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2024.109470

Authors: Waqar Ahmed, Panos Liatsis

Institution: Khalifa University of Science and Technology

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,640