MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation.

Background: Capturing the segmentation of blood vessels by a fundus camera is crucial for the medical evaluation of various retinal vascular issues. However, due to the complicated vascular structure and unclear clinical criteria, the precise segmentation of blood arteries remains very challenging.

Methods: To address this issue, we developed the upgraded multi-convolution block and squeeze and excitation based on the U-shape network (MCSE-U-net) model that segments retinal vessels using a U-shaped network. This model uses multi-convolution (MC) blocks, squeeze and excitation (SE) blocks, and squeeze blocks. First, the input image was processed using the luminance, chrominance-blue, chrominance-red (YCbCr) color conversion method to further improve visibility. Second, a MC module was added to increase the model's ability to accurately segment blood vessels. Third, SE blocks were added to enhance the network model's ability to segment fine blood vessels in medical images.

Results: The suggested architecture was assessed using evaluation metrics, including the Dice coefficient, sensitivity (sen), specificity (spe), accuracy (acc), and mean intersection over union (mIoU), on an open-source Digital Retinal Images for Vessel Extraction (DRIVE) data set. The outcomes showed the effectiveness of the suggested approach, particularly in the extraction of peripheral vascular anatomy. Using the suggested architecture, the model had a Dice coefficient of 0.8430, a sen of 0.8752, a spe of 0.9902, an acc of 0.9725, and a mIoU of 0.8473 for the DRIVE data set. The Dice coefficient, sen, spe, acc, and mIoU of the MCSE-U-net increased by 3.08%, 6.22%, 0.62%, 0.61%, and 3.01%, respectively, compared to the original U-net, demonstrating the better all-around performance of the MCSE-U-net.

Conclusions: The MCSE-U-net network performed and achieved more than the technologies already in use.