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BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation
Yugen Yi1, Changlu Guo2, Yangtao Hu3
1School of Software, Jiangxi Normal University, Nanchang, China.
Insights
The novel Bi-directional ConvLSTM Residual U-Net (BCR-UNet) effectively segments tiny retinal blood vessels in low-contrast areas. This method significantly improves the detection of vessels crucial for diagnosing diseases like glaucoma.
Area of Science:
- Medical imaging analysis
- Deep learning for medical diagnostics
- Ophthalmology and cardiovascular disease research
Background:
- Accurate retinal blood vessel segmentation is vital for diagnosing conditions like glaucoma and cardiovascular disease.
- Existing U-Net based methods struggle to segment low-contrast, tiny vessels in peripheral retinal regions.
- Preserving fine vascular structures is critical for early disease detection.
Purpose of the Study:
- To develop an advanced deep learning model for high-precision retinal blood vessel segmentation.
- To address the challenge of segmenting low-contrast, tiny vessels in peripheral retinal images.
- To improve the robustness and discriminative ability of retinal image segmentation networks.
Main Methods:
- Proposed a novel network: Bi-directional ConvLSTM Residual U-Net (BCR-UNet).
- Introduced Structured Dropout Residual Blocks (SDRB) for enhanced network robustness.
- Utilized Bi-directional ConvLSTM (BConvLSTM) to integrate feature maps for preserving tiny vessel information.
Main Results:
- BCR-UNet demonstrated superior performance in preserving tiny blood vessels in low-contrast peripheral regions.
- Experimental results on four public datasets confirmed the effectiveness of the proposed method.
- The model outperformed previous state-of-the-art methods in retinal blood vessel segmentation.
Conclusions:
- The BCR-UNet model significantly enhances the segmentation of delicate retinal vasculature.
- This advancement aids in more accurate diagnosis of eye and cardiovascular diseases.
- The proposed method offers a robust solution for challenging retinal image segmentation tasks.
Background:
High precision segmentation of retinal blood vessels from retinal images is a significant step for doctors to diagnose many diseases such as glaucoma and cardiovascular diseases. However, at the peripheral region of vessels, previous U-Net-based segmentation methods failed to significantly preserve the low-contrast tiny vessels.
Methods:
For solving this challenge, we propose a novel network model called Bi-directional ConvLSTM Residual U-Net (BCR-UNet), which takes full advantage of U-Net, Dropblock, Residual convolution and Bi-directional ConvLSTM (BConvLSTM). In this proposed BCR-UNet model, we propose a novel Structured Dropout Residual Block (SDRB) instead of using the original U-Net convolutional block, to construct our network skeleton for improving the robustness of the network. Furthermore, to improve the discriminative ability of the network and preserve more original semantic information of tiny vessels, we adopt BConvLSTM to integrate the feature maps captured from the first residual block and the last up-convolutional layer in a nonlinear manner.
Results And Discussion:
We conduct experiments on four public retinal blood vessel datasets, and the results show that the proposed BCR-UNet can preserve more tiny blood vessels at the low-contrast peripheral regions, even outperforming previous state-of-the-art methods.
