# IterMiUnet: A lightweight architecture for automatic blood vessel segmentation Source: [IterMiUnet: A lightweight architecture for automatic blood vessel segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:13:37.852Z Authors: Ashish Kumar, R.K. Agrawal, Leve Joseph Published: 2022-08-02T14:33:14.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Retinal Vessel Segmentation on CHASE\_DB1 (test)](): Retinal Vessel Segmentation; CHASE\_DB1 (test); Accuracy: 0.9646. - [Retinal Vessel Segmentation on DRIVE](): Retinal Vessel Segmentation; DRIVE; Accuracy (AC): 0.9575. ## Original sources and implementation [Original paper]() [PDF]()