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- Publisher Website: 10.1007/978-3-030-00931-1_58
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Conference Paper: Automatic irregular texture detection in brain MRI without human supervision
Title | Automatic irregular texture detection in brain MRI without human supervision |
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Authors | |
Keywords | MRI Hyperintensities detection Irregular texture detection Unsupervised detection |
Issue Date | 2018 |
Publisher | Springer. |
Citation | 21st International Conferenceon Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), Granada, Spain, 16-20 September 2018. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: Part III, 2018, p. 506-513 How to Cite? |
Abstract | © Springer Nature Switzerland AG 2018. We propose a novel approach named one-time sampling irregularity age map (OTS-IAM) to detect any irregular texture in FLAIR brain MRI without any human supervision or interaction. In this study, we show that OTS-IAM is able to detect FLAIR’s brain tissue irregularities (i.e. hyperintensities) without any manual labelling. One-time sampling (OTS) scheme is proposed in this study to speed up the computation. The proposed OTS-IAM implementation on GPU successfully speeds up IAM’s computation by more than 17 times. We compared the performance of OTS-IAM with two unsupervised methods for hyperintensities’ detection; the original IAM and the Lesion Growth Algorithm from public toolbox Lesion Segmentation Toolbox (LST-LGA), and two conventional supervised machine learning algorithms; support vector machine (SVM) and random forest (RF). Furthermore, we also compared OTS-IAM’s performance with three supervised deep neural networks algorithms; Deep Boltzmann machine (DBM), convolutional encoder network (CEN) and 2D convolutional neural network (2D Patch-CNN). Based on our experiments, OTS-IAM outperformed LST-LGA, SVM, RF and DBM while it was on par with CEN. |
Persistent Identifier | http://hdl.handle.net/10722/288754 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science ; 11072 |
DC Field | Value | Language |
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dc.contributor.author | Rachmadi, Muhammad Febrian | - |
dc.contributor.author | Valdés-Hernández, Maria del C. | - |
dc.contributor.author | Komura, Taku | - |
dc.date.accessioned | 2020-10-12T08:05:47Z | - |
dc.date.available | 2020-10-12T08:05:47Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 21st International Conferenceon Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), Granada, Spain, 16-20 September 2018. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: Part III, 2018, p. 506-513 | - |
dc.identifier.isbn | 9783030009304 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288754 | - |
dc.description.abstract | © Springer Nature Switzerland AG 2018. We propose a novel approach named one-time sampling irregularity age map (OTS-IAM) to detect any irregular texture in FLAIR brain MRI without any human supervision or interaction. In this study, we show that OTS-IAM is able to detect FLAIR’s brain tissue irregularities (i.e. hyperintensities) without any manual labelling. One-time sampling (OTS) scheme is proposed in this study to speed up the computation. The proposed OTS-IAM implementation on GPU successfully speeds up IAM’s computation by more than 17 times. We compared the performance of OTS-IAM with two unsupervised methods for hyperintensities’ detection; the original IAM and the Lesion Growth Algorithm from public toolbox Lesion Segmentation Toolbox (LST-LGA), and two conventional supervised machine learning algorithms; support vector machine (SVM) and random forest (RF). Furthermore, we also compared OTS-IAM’s performance with three supervised deep neural networks algorithms; Deep Boltzmann machine (DBM), convolutional encoder network (CEN) and 2D convolutional neural network (2D Patch-CNN). Based on our experiments, OTS-IAM outperformed LST-LGA, SVM, RF and DBM while it was on par with CEN. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: Part III | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 11072 | - |
dc.subject | MRI | - |
dc.subject | Hyperintensities detection | - |
dc.subject | Irregular texture detection | - |
dc.subject | Unsupervised detection | - |
dc.title | Automatic irregular texture detection in brain MRI without human supervision | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-00931-1_58 | - |
dc.identifier.scopus | eid_2-s2.0-85053932410 | - |
dc.identifier.spage | 506 | - |
dc.identifier.epage | 513 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000477769700058 | - |
dc.publisher.place | Cham, Switzerland | - |
dc.identifier.issnl | 0302-9743 | - |