  Blinding eye diseases are often correlated with altered retinal morphology,
which can be clinically identified by segmenting retinal structures in fundus
images. However, current methodologies often fall short in accurately
segmenting delicate vessels. Although deep learning has shown promise in
medical image segmentation, its reliance on repeated convolution and pooling
operations can hinder the representation of edge information, ultimately
limiting overall segmentation accuracy. In this paper, we propose a lightweight
pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an
exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The
network used multipath feature extraction blocks and incorporates bidirectional
skip connections for the information flow between the encoder and decoder.
Additionally, we have optimized the efficiency of the model by carefully
selecting the number of filters to avoid filter overlap. This optimization
significantly reduces training time and enhances computational efficiency. To
assess the robustness and generalizability of LMBiS-Net, we performed
comprehensive evaluations on various aspects of retinal images. Specifically,
the model was subjected to rigorous tests to accurately segment retinal
vessels, which play a vital role in ophthalmological diagnosis and treatment.
By focusing on the retinal blood vessels, we were able to thoroughly analyze
the performance and effectiveness of the LMBiS-Net model. The results of our
tests demonstrate that LMBiS-Net is not only robust and generalizable but also
capable of maintaining high levels of segmentation accuracy. These
characteristics highlight the potential of LMBiS-Net as an efficient tool for
high-speed and accurate segmentation of retinal images in various clinical
applications.
