IterMiUnet: A lightweight architecture for automatic blood vessel
segmentation
- URL: http://arxiv.org/abs/2208.01485v1
- Date: Tue, 2 Aug 2022 14:33:14 GMT
- Title: IterMiUnet: A lightweight architecture for automatic blood vessel
segmentation
- Authors: Ashish Kumar, R.K. Agrawal, Leve Joseph
- Abstract summary: This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model.
It overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it.
The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
- Score: 10.538564380139483
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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