Enhancing Retinal Vascular Structure Segmentation in Images With a Novel
  Design Two-Path Interactive Fusion Module Model
Rui Yang Shunpu Zhang
Precision in identifying and differentiating micro and macro blood vessels in
the retina is crucial for the diagnosis of retinal diseases, although it poses
a significant challenge. Current autoencoding-based segmentation approaches
encounter limitations as they are constrained by the encoder and undergo a
reduction in resolution during the encoding stage. The inability to recover
lost information in the decoding phase further impedes these approaches.
Consequently, their capacity to extract the retinal microvascular structure is
restricted. To address this issue, we introduce Swin-Res-Net, a specialized
module designed to enhance the precision of retinal vessel segmentation.
Swin-Res-Net utilizes the Swin transformer which uses shifted windows with
displacement for partitioning, to reduce network complexity and accelerate
model convergence. Additionally, the model incorporates interactive fusion with
a functional module in the Res2Net architecture. The Res2Net leverages
multi-scale techniques to enlarge the receptive field of the convolutional
kernel, enabling the extraction of additional semantic information from the
image. This combination creates a new module that enhances the localization and
separation of micro vessels in the retina. To improve the efficiency of
processing vascular information, we've added a module to eliminate redundant
information between the encoding and decoding steps.
  Our proposed architecture produces outstanding results, either meeting or
surpassing those of other published models. The AUC reflects significant
enhancements, achieving values of 0.9956, 0.9931, and 0.9946 in pixel-wise
segmentation of retinal vessels across three widely utilized datasets:
CHASE-DB1, DRIVE, and STARE, respectively. Moreover, Swin-Res-Net outperforms
alternative architectures, demonstrating superior performance in both IOU and
F1 measure metrics.