Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging
Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and...
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Institute of Electrical and Electronics Engineers Inc.
2017
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my.utp.eprints.200232018-04-22T14:37:58Z Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging Lemaitre, G. Marti, R. Rastgoo, M. Meriaudeau, F. Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer location in the prostate. We extensively tested our proposed CAD using different fusion approaches to combine the features provided by each modality. The source code and the dataset have been released. © 2017 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032226643&doi=10.1109%2fEMBC.2017.8037522&partnerID=40&md5=fa9baf1447ac7eb55a334b8e06f877dc Lemaitre, G. and Marti, R. and Rastgoo, M. and Meriaudeau, F. (2017) Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS . pp. 3138-3141. http://eprints.utp.edu.my/20023/ |
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Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer location in the prostate. We extensively tested our proposed CAD using different fusion approaches to combine the features provided by each modality. The source code and the dataset have been released. © 2017 IEEE. |
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Article |
author |
Lemaitre, G. Marti, R. Rastgoo, M. Meriaudeau, F. |
spellingShingle |
Lemaitre, G. Marti, R. Rastgoo, M. Meriaudeau, F. Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
author_facet |
Lemaitre, G. Marti, R. Rastgoo, M. Meriaudeau, F. |
author_sort |
Lemaitre, G. |
title |
Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
title_short |
Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
title_full |
Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
title_fullStr |
Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
title_full_unstemmed |
Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
title_sort |
computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging |
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Institute of Electrical and Electronics Engineers Inc. |
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2017 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032226643&doi=10.1109%2fEMBC.2017.8037522&partnerID=40&md5=fa9baf1447ac7eb55a334b8e06f877dc http://eprints.utp.edu.my/20023/ |
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