A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation

A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation

Osteogenesis Imperfecta (OI) image segmentation by using Deep Convolutional Neural Network (DCNN) is yet to be evaluated. The segmentation of OI is very important as a useful tool for medical experts to further analyze the fracture risk and avoid bone fractures. In this paper, we present the review...

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Published in:Lecture Notes in Electrical Engineering
Main Author: Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
Format: Conference paper
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125284492&doi=10.1007%2f978-981-16-8129-5_148&partnerID=40&md5=2fbb20fafc7e5508551ba4e61ab1d9af
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Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
2022
Lecture Notes in Electrical Engineering
829 LNEE

10.1007/978-981-16-8129-5_148
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125284492&doi=10.1007%2f978-981-16-8129-5_148&partnerID=40&md5=2fbb20fafc7e5508551ba4e61ab1d9af
Osteogenesis Imperfecta (OI) image segmentation by using Deep Convolutional Neural Network (DCNN) is yet to be evaluated. The segmentation of OI is very important as a useful tool for medical experts to further analyze the fracture risk and avoid bone fractures. In this paper, we present the review of DCNN architecture used in image segmentation. The images were obtained from different types of modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound. Several architectures have been used by previous studies include U-Net, faster R-CNN, ResNet, and MS-Net architecture to automatically segment the images. Overall, all researchers from the reviewed papers concluded that the proposed DCNN architecture gave good performance results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Springer Science and Business Media Deutschland GmbH
18761100
English
Conference paper
author Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
spellingShingle Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
author_facet Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
author_sort Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
title A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
title_short A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
title_full A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
title_fullStr A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
title_full_unstemmed A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
title_sort A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation
publishDate 2022
container_title Lecture Notes in Electrical Engineering
container_volume 829 LNEE
container_issue
doi_str_mv 10.1007/978-981-16-8129-5_148
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125284492&doi=10.1007%2f978-981-16-8129-5_148&partnerID=40&md5=2fbb20fafc7e5508551ba4e61ab1d9af
description Osteogenesis Imperfecta (OI) image segmentation by using Deep Convolutional Neural Network (DCNN) is yet to be evaluated. The segmentation of OI is very important as a useful tool for medical experts to further analyze the fracture risk and avoid bone fractures. In this paper, we present the review of DCNN architecture used in image segmentation. The images were obtained from different types of modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound. Several architectures have been used by previous studies include U-Net, faster R-CNN, ResNet, and MS-Net architecture to automatically segment the images. Overall, all researchers from the reviewed papers concluded that the proposed DCNN architecture gave good performance results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher Springer Science and Business Media Deutschland GmbH
issn 18761100
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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