Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different...

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Bibliographic Details
Published in:Diagnostics (Basel) Vol. 12; no. 12; p. 3064
Main Authors: Krithika Alias AnbuDevi, M, Suganthi, K
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01-12-2022
MDPI
Subjects:
Brain research
CNN
CT imaging
Deep learning
Diagnostic imaging
dice similarity coefficient
Image segmentation
Medical imaging equipment
MRI
Neural networks
Review
semantic segmentation
Semantics
Technology application
UNET
India
dice similarity coefficient
CNN
semantic segmentation
MRI
UNET
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Summary:In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12123064