# LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation

arXiv: https://arxiv.org/abs/2309.04968 · HTML: https://mlsanity.com/paper/2309.04968/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://mlsanity.com/paper/1909.00166/) (2019)
- [CNN-based Segmentation of Medical Imaging Data](https://mlsanity.com/paper/1701.03056/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling](https://mlsanity.com/paper/1505.07293/) (2015)
- [BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture](https://mlsanity.com/paper/2007.00243/) (2020)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2112.11078/) (2021)
- [Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation](https://mlsanity.com/paper/2309.03535/) (2023)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments](https://mlsanity.com/paper/1811.07738/) (2018)
- [LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms](https://mlsanity.com/paper/2306.06145/) (2023)

## Built on by (most cited first, 6 of 6)

- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation](https://mlsanity.com/paper/2409.03367/) (2024)
- [LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation](https://mlsanity.com/paper/2407.02871/) (2024)
- [Region Guided Attention Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2407.18970/) (2024)
- [LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis](https://mlsanity.com/paper/2412.05968/) (2024)
- [LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio](https://mlsanity.com/paper/2509.11811/) (2025)

## Abstract (arXiv)

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 (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.