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Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network

The number of prostate cancer cases is steadily increasing especially with rising number of ageing population. It is reported that 5-year relative survival rate for man with stage 1 prostate cancer is almost 99 hence, early detection will significantly improve treatment planning and increase surviva...

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Main Authors: Khan, Z., Yahya, N., Alsaih, K., Meriaudeau, F.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075639613&doi=10.1109%2fSCORED.2019.8896248&partnerID=40&md5=512bc1f51c5a42baf95bd37f1e31bfe4
http://eprints.utp.edu.my/24891/
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spelling my.utp.eprints.248912021-08-27T08:45:17Z Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network Khan, Z. Yahya, N. Alsaih, K. Meriaudeau, F. The number of prostate cancer cases is steadily increasing especially with rising number of ageing population. It is reported that 5-year relative survival rate for man with stage 1 prostate cancer is almost 99 hence, early detection will significantly improve treatment planning and increase survival rate. Magnetic resonance imaging (MRI) technique is a common imaging modality for diagnosis of prostate cancer. MRI provide good visualization of soft tissue and enable better lesion detection and staging of prostate cancer. The main challenge of prostate whole gland segmentation is due to blurry boundary of central gland (CG) and peripheral zone (PZ) which lead to differential diagnosis. Since there is substantial difference in occurance and characteristic of cancer in both zones. So to enhance the diagnosis of prostate gland, we implemented DeeplabV3+ semantic segmentation approach to segment the prostate into zones. DeepLabV3+ achieved significant results in segmentation of prostate MRI by applying several parallel atrous convolution with different rates. The CNN-based semantic segmentation approach is trained and tested on NCI-ISBI 1.5T and 3T MRI dataset consist of 40 patients. Performance evaluation based on Dice similarity coefficient (DSC) of the Deeplab-based segmentation is compared with two other CNN-based semantic segmentation techniques: FCN and PSNet. Results shows that prostate segmentation using DeepLabV3+ can perform better than FCN and PSNet with average DSC of 70.3 in PZ and 88 in CG zone. This indicates the significant contribution made by the atrous convolution layer, in producing better prostate segmentation result. © 2019 IEEE. Institute of Electrical and Electronics Engineers Inc. 2019 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075639613&doi=10.1109%2fSCORED.2019.8896248&partnerID=40&md5=512bc1f51c5a42baf95bd37f1e31bfe4 Khan, Z. and Yahya, N. and Alsaih, K. and Meriaudeau, F. (2019) Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network. In: UNSPECIFIED. http://eprints.utp.edu.my/24891/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The number of prostate cancer cases is steadily increasing especially with rising number of ageing population. It is reported that 5-year relative survival rate for man with stage 1 prostate cancer is almost 99 hence, early detection will significantly improve treatment planning and increase survival rate. Magnetic resonance imaging (MRI) technique is a common imaging modality for diagnosis of prostate cancer. MRI provide good visualization of soft tissue and enable better lesion detection and staging of prostate cancer. The main challenge of prostate whole gland segmentation is due to blurry boundary of central gland (CG) and peripheral zone (PZ) which lead to differential diagnosis. Since there is substantial difference in occurance and characteristic of cancer in both zones. So to enhance the diagnosis of prostate gland, we implemented DeeplabV3+ semantic segmentation approach to segment the prostate into zones. DeepLabV3+ achieved significant results in segmentation of prostate MRI by applying several parallel atrous convolution with different rates. The CNN-based semantic segmentation approach is trained and tested on NCI-ISBI 1.5T and 3T MRI dataset consist of 40 patients. Performance evaluation based on Dice similarity coefficient (DSC) of the Deeplab-based segmentation is compared with two other CNN-based semantic segmentation techniques: FCN and PSNet. Results shows that prostate segmentation using DeepLabV3+ can perform better than FCN and PSNet with average DSC of 70.3 in PZ and 88 in CG zone. This indicates the significant contribution made by the atrous convolution layer, in producing better prostate segmentation result. © 2019 IEEE.
format Conference or Workshop Item
author Khan, Z.
Yahya, N.
Alsaih, K.
Meriaudeau, F.
spellingShingle Khan, Z.
Yahya, N.
Alsaih, K.
Meriaudeau, F.
Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
author_facet Khan, Z.
Yahya, N.
Alsaih, K.
Meriaudeau, F.
author_sort Khan, Z.
title Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
title_short Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
title_full Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
title_fullStr Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
title_full_unstemmed Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network
title_sort zonal segmentation of prostate t2w-mri using atrous convolutional neural network
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075639613&doi=10.1109%2fSCORED.2019.8896248&partnerID=40&md5=512bc1f51c5a42baf95bd37f1e31bfe4
http://eprints.utp.edu.my/24891/
_version_ 1738656653969981440
score 13.211869

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