WITHDRAWN: LSAC-Net: A lightweight scale-aware CNN with densely connected focal modulation for retinal blood vessel segmentation

Abstract

The diagnosis of sight-threatening eye conditions typically involves segmenting retinal structures in fundus images. The segmentation process enables clinicians to identify alterations in retinal morphology, facilitating the detection and diagnosis of various eye diseases. Despite recent developments in image processing, current approaches often fail to partition delicate arteries precisely. Although deep learning has great potential for segmenting medical images, the accuracy of the segmentation process may be limited due to its dependence on repetitive convolution and pooling processes, which may impair the representation of edge information. In this study, we present LSAC-Net, a pixel-level convolutional neural network (CNN) designed specifically for retinal vessel segmentation. Moreover, it is lightweight, with only 37.5 K learnable parameters. LSAC-Net integrates a unique scale-aware feature extraction block (SAFEB) and a densely connected focal modulation block (DCFMB) to improve segmentation accuracy. In addition, careful selection of filter numbers to avoid overlap effectively shortens training times and boosts computational performance, both of which improve overall model efficiency. We performed extensive tests on different aspects of retinal images to evaluate the robustness and generalizability of LSAC-Net. In particular, a thorough evaluation of the model's precision in retinal artery segmentation, a crucial task for ophthalmological diagnosis and treatment, was carried out. We evaluated the performance and efficacy of LSAC-Net with a particular focus on RBV. The model obtains 98.65% specificity, 97.54% accuracy, and 98.88% area under the curve surpassing existing approaches. Experimental results confirm that LSAC-Net is robust, generalizable, and maintains a high level of segmentation accuracy. These features highlight LSAC-Net's potential as a useful instrument for accurate and timely retinal image segmentation in a variety of therapeutic contexts.