Authors:
Vijaya Kamble
1
and
Rohin Daruwala
2
Affiliations:
1
Research Scholar Department of Electronics Engineering, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra, India
;
2
Department of Electronics Engineering, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra, India
Keyword(s):
3D UNet, MRI Images, Segmentation, Brain Tumors.
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.
(More)