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Attention adaptive instance normalization style transfer for vascular segmentation using deep learning

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Abstract

Deep learning models have demonstrated substantial progress in medical image segmentation. However, these models require large datasets for training, which can prove to be clinically difficult. Medical imaging datasets exhibit domain shift problems due to different imaging techniques, scanners, and data privacy issues and, conventional deep neural networks lack generalization capabilities, as their effectiveness decreases with different data distributions. This paper presents a deep learning-based attention-adaptive instance normalization style transfer technique to address the challenges encountered when segmenting blood vessels. The proposed methodology combines adaptive instance normalization style transfer with a dense extreme inception network and convolution block attention module to achieve the best observed vessel segmentation performance. A simple yet effective method is proposed, and it improves the generalization performance of deep neural networks in vascular segmentation. The network is trained on natural images and tested on medical images, thereby overcoming the need for a large dataset or labelled ground truth to train for vessel segmentation. The proposed technique uses experimental results from five distinct medical datasets to demonstrate higher cross-domain generalization capabilities than the state-of-the-art baselines available in the current literature, and the segmentation performance is compared qualitatively and quantitatively with other models. The results demonstrate the feasibility of generalizing our approach to various datasets. This approach overcomes the constraints of traditional deep learning algorithms, which require enormous volumes of medical data along with manually-labelled ground truth. The predictions by the proposed approach are based on natural image training and can be reliably used to detect and identify cardiac and retinal abnormalities without prior medical imaging information.

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Data availability and access

All data relevant to this research are available from [26, 40, 41], and [42].

Notes

  1. https://github.com/naoto0804/pytorch-AdaIN

  2. https://github.com/Jongchan/attention-module

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Acknowledgements

We would like to thank Prof. Shantanu Mulay for providing their valuable comments and suggestions.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India

    Supriti Mulay & Mohanasankar Sivaprakasam

  2. Healthcare Technology Innovation Centre, Chennai, India

    Supriti Mulay, Keerthi Ram & Mohanasankar Sivaprakasam

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  1. Supriti Mulay
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  3. Mohanasankar Sivaprakasam
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Contributions

Supriti Mulay: Conceptualization, Methodology, Software, Validation, Writing - original draft. Keerthi Ram: Conceptualization, Writing - review & editing, Resources. Mohanasankar Sivaprakasam: Supervision, Project administration, Funding acquisition.

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Correspondence to Supriti Mulay.

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Mulay, S., Ram, K. & Sivaprakasam, M. Attention adaptive instance normalization style transfer for vascular segmentation using deep learning. Appl Intell 53, 29638–29655 (2023). https://doi.org/10.1007/s10489-023-05033-1

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