Hierarchical Feature Integrated BoT-UNet with contextual feature enhancement for retinal vessel segmentation

Retinal vessels provide crucial information about eye health and systemic diseases.Thin vessels are particularly important as they can indicate early signs of diseases.The challenges with thin vessels are that they have low contrast and can be easily confused with background noise.Variations in vessel width and intensity make uniform detection difficult.Presence of other retinal structures (e.g., optic disc, fovea) can further complicate segmentation.In the context of retinal vessel segmentation, attention can help the model focus more on areas likely to contain thin vessels.To ensure feature diversity, it's crucial to incorporate both fine-grained details (like edges and textures) and broader, more complex elements (such as intricate objects or structural components).In our paper, we propose a Hierarchical Feature Integrated BoT-UNet with contextual feature enhancement.The proposed architecture preserves U-Net's core design while innovating its components.Both encoder and decoder sections are substituted with the implementation of bottleneck transformer structures, a modification that substantially decreases computational demands compared to conventional Transformer models.Furthermore, the proposed BoT U-Net incorporates a fusion of convolutional and Transformer elements (hybrid), eliminating the need for pre-trained weights.The architecture employs Hierarchical Feature Integration Block (HFIB) and integrates Contextual Feature Enhancement Block (CFEB) between the encoding and decoding stages to enhance segmentation performance.Additionally, backward fusion mechanisms are employed to aggregate and reintegrate context, enhancing the feature refinement process.This bidirectional fusion ensures effective capture and utilization of both local and global contexts.The proposed network achieves competitive retinal segmentation results on three public benchmark datasets: DRIVE, CHASE DB1, and STARE.