Modality specific U-Net variants for biomedical image segmentation: A
survey
- URL: http://arxiv.org/abs/2107.04537v1
- Date: Fri, 9 Jul 2021 16:41:40 GMT
- Title: Modality specific U-Net variants for biomedical image segmentation: A
survey
- Authors: Narinder Singh Punn, Sonali Agarwal
- Abstract summary: This article contributes to describe the U-Net framework, followed by the comprehensive analysis of the U-Net variants for different medical imaging or modalities.
Also highlights the contribution of U-Net based frameworks in the on-going pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
- Score: 0.6091702876917281
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advent of advancements in deep learning approaches, such as deep
convolution neural network, residual neural network, adversarial network; U-Net
architectures are most widely utilized in biomedical image segmentation to
address the automation in identification and detection of the target regions or
sub-regions. In recent studies, U-Net based approaches have illustrated
state-of-the-art performance in different applications for the development of
computer-aided diagnosis systems for early diagnosis and treatment of diseases
such as brain tumor, lung cancer, alzheimer, breast cancer, etc. This article
contributes to present the success of these approaches by describing the U-Net
framework, followed by the comprehensive analysis of the U-Net variants for
different medical imaging or modalities such as magnetic resonance imaging,
X-ray, computerized tomography/computerized axial tomography, ultrasound,
positron emission tomography, etc. Besides, this article also highlights the
contribution of U-Net based frameworks in the on-going pandemic, severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
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