MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation
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Updated: Jul 9, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Lihong Zhang1, Chongxin Xu1, Yuzhuo Li1
1College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.
Insights
The MCSE-U-net model accurately segments retinal blood vessels, outperforming existing methods. This advancement improves the detection of retinal vascular issues from fundus images.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Accurate segmentation of retinal blood vessels from fundus images is critical for diagnosing various retinal vascular diseases.
- The complex vascular structure and ambiguous clinical criteria present significant challenges in precise blood vessel segmentation.
Purpose of the Study:
- To develop an advanced deep learning model for enhanced retinal vessel segmentation.
- To improve the accuracy and efficiency of automated retinal vessel segmentation in medical imaging.
Main Methods:
- Developed the MCSE-U-net model, integrating multi-convolution (MC) and squeeze-and-excitation (SE) blocks into a U-shaped network architecture.
- Employed YCbCr color space conversion for improved image visibility and utilized MC and SE blocks to enhance segmentation of both major and fine retinal vessels.
Main Results:
- The MCSE-U-net model achieved high performance on the DRIVE dataset, with a Dice coefficient of 0.8430, sensitivity of 0.8752, specificity of 0.9902, accuracy of 0.9725, and mIoU of 0.8473.
- Demonstrated significant improvements over the original U-net, with increases of 3.08% in Dice coefficient, 6.22% in sensitivity, 0.62% in specificity, 0.61% in accuracy, and 3.01% in mIoU.
- The model showed particular effectiveness in segmenting peripheral vascular anatomy.
Conclusions:
- The MCSE-U-net model offers superior performance for retinal vessel segmentation compared to existing technologies.
- The proposed architecture effectively addresses the challenges in segmenting complex retinal vascular structures.
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.
