IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

TL;DR

IterMiUnet is a lightweight, efficient deep learning model for blood vessel segmentation in fundus images, maintaining high performance with fewer parameters, suitable for medical applications with limited data.

Contribution

The paper introduces IterMiUnet, a novel lightweight convolutional model that combines Iternet and MiUnet architectures to reduce parameters without sacrificing segmentation accuracy.

Findings

Achieved comparable performance to larger models on DRIVE, STARE, and CHASE-DB1 datasets.

Reduced training and inference times due to fewer parameters.

Demonstrated potential for early disease diagnosis with efficient segmentation.

Abstract

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.