3D MRI Image Segmentation using 3D UNet Architectures: Technical Review

Vijaya Kamble, Rohin Daruwala

2022

Abstract

From last few decades machine learning & deep convolutional neural networks (CNNs) used extensively and have shown remarkable performance in almost all fields including medical diagnostics. It is used in medical domain for automatic tissue, lesion detection, segmentation, anatomical or structure segmentation classification & survival predictions. In this paper we presented an extensive technical literature review on 3D CNN U-Net architectures applied for 3D brain magnetic resonance imaging (MRI) analysis. We mainly focused on the architectures, its modifications, pre-processing techniques, types datasets, data preparation, methodology, GPU, tumor disease types and per architectures evaluation measures in this works. Our primary goal for this extensive technical review is to report how different 3D U-Net architectures or CNN architectures have been used to differentiate between state-of-the-art strategies, compare their results obtained using public/clinical datasets and examine their effectiveness. This paper is intended to present detailed reference for further research activity or plan of strategy to use 3D U-Nets for brain MRI automated tumor diseases detection, segmentation & survival prediction analysis. Finally, we are presenting a novel perspective to assist research directions on the future of CNNs & 3D U-Net architectures to explore in subsequent years to help doctors & radiologist.

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Paper Citation


in Harvard Style

Kamble V. and Daruwala R. (2022). 3D MRI Image Segmentation using 3D UNet Architectures: Technical Review. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-552-4, pages 141-146. DOI: 10.5220/0010851300003123


in Bibtex Style

@conference{bioimaging22,
author={Vijaya Kamble and Rohin Daruwala},
title={3D MRI Image Segmentation using 3D UNet Architectures: Technical Review},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2022},
pages={141-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010851300003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - 3D MRI Image Segmentation using 3D UNet Architectures: Technical Review
SN - 978-989-758-552-4
AU - Kamble V.
AU - Daruwala R.
PY - 2022
SP - 141
EP - 146
DO - 10.5220/0010851300003123