Implementation
of
LMBiS-Net
Implementation of Abbasi et al’s Paper: “LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation”
Milad Farazian, Charlie Floeder, Rizq Khateeb, Harshit Shah, Yash Sharma
Original Paper
PROJECT GOAL
To implement the LMBiS-Net model and confirm the findings presented in “LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation.” A second goal is to apply our implementation to a different dataset that is not used in the paper.
PROJECT IMPORTANCE
LMBiS-Net’s primary benefit is it provides an accurate retinal blood vessel segmentation model that is more computationally efficient compared to current state-of-the-art models. Retinal blood vessel segmentation is beneficial for early detection and treatment of retinal diseases. This model can significantly reduce the amount of time ophthalmologists spend manually identifying retinal vessels. Additionally, the model can reduce human error in that task. Retinal diseases are a major cause of visual impairment and blindness. Studies show that 5%-20% of the global population ages 40+ have retinal disorders. Examining retinal vessels can provide important information regarding the underlying medical conditions leading to retinal diseases.
LMBiS-NET
LMBiS-Net is a CNN consisting of three encoder blocks, a bottleneck layer, and three decoder blocks. The network uses multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoders and decoders.
MULTI-PATH FEATURE EXTRACTION BLOCK
This is a key component to LMBiS-Net, which introduces feature diversity into the model and helps prevent overfitting. By processing the data using different sized convolutions, the network can learn to capture both low-level and high-level features which is important for blood vessel segmentation.
PROJECT CONTRIBUTION
We created the first publicly available implementation of LMBiS-Net along with code to augment retinal images to increase the size of training datasets. Our findings add credibility to the original paper’s claims that LMBiS-Net is a computationally efficient and accurate state-of-the-art retinal blood vessel segmentation model.
RESULTS
Google Colab