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- Publisher Website: 10.1007/978-3-319-60964-5_42
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Conference Paper: Evaluation of four supervised learning schemes in white matter hyperintensities segmentation in absence or mild presence of vascular pathology
Title | Evaluation of four supervised learning schemes in white matter hyperintensities segmentation in absence or mild presence of vascular pathology |
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Authors | |
Keywords | Deep neural network White matter hyperintensities Segmentation Supervised learning Brain MRI |
Issue Date | 2017 |
Publisher | Springer. |
Citation | 21st Annual Conference on Medical Image Understanding and Analysis (MIUA 2017), Edinburgh, UK, 11-13 July 2017. In Medical Image Understanding and Analysis, 2017, p. 482-493 How to Cite? |
Abstract | We investigated the performance of four popular supervised learning algorithms in medical image analysis for white matter hyperintensities segmentation in brain MRI with mild or no vascular pathology. The algorithms evaluated in this study are support vector machine (SVM), random forest (RF), deep Boltzmann machine (DBM) and convolution encoder network (CEN). We compared these algorithms with two methods in the Lesion Segmentation Tool (LST) public toolbox which are lesion growth algorithm (LGA) and lesion prediction algorithm (LPA). We used a dataset comprised of 60 MRI data from 20 subjects from the ADNI database, each scanned once in three consecutive years. In this study, CEN produced the best Dice similarity coefficient (DSC): mean value 0.44. All algorithms struggled to produce good DSC due to the very small WMH burden (i.e., smaller than 1,500 mm3). LST-LGA, LST-LPA, SVM, RF and DBM produced mean DSC scores ranging from 0.17 to 0.34. |
Persistent Identifier | http://hdl.handle.net/10722/288863 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.203 |
ISI Accession Number ID | |
Series/Report no. | Communications in Computer and Information Science ; 723 |
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 | Agan, Maria Leonora Fatimah | - |
dc.contributor.author | Komura, Taku | - |
dc.date.accessioned | 2020-10-12T08:06:04Z | - |
dc.date.available | 2020-10-12T08:06:04Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 21st Annual Conference on Medical Image Understanding and Analysis (MIUA 2017), Edinburgh, UK, 11-13 July 2017. In Medical Image Understanding and Analysis, 2017, p. 482-493 | - |
dc.identifier.isbn | 9783319609638 | - |
dc.identifier.issn | 1865-0929 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288863 | - |
dc.description.abstract | We investigated the performance of four popular supervised learning algorithms in medical image analysis for white matter hyperintensities segmentation in brain MRI with mild or no vascular pathology. The algorithms evaluated in this study are support vector machine (SVM), random forest (RF), deep Boltzmann machine (DBM) and convolution encoder network (CEN). We compared these algorithms with two methods in the Lesion Segmentation Tool (LST) public toolbox which are lesion growth algorithm (LGA) and lesion prediction algorithm (LPA). We used a dataset comprised of 60 MRI data from 20 subjects from the ADNI database, each scanned once in three consecutive years. In this study, CEN produced the best Dice similarity coefficient (DSC): mean value 0.44. All algorithms struggled to produce good DSC due to the very small WMH burden (i.e., smaller than 1,500 mm3). LST-LGA, LST-LPA, SVM, RF and DBM produced mean DSC scores ranging from 0.17 to 0.34. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Medical Image Understanding and Analysis | - |
dc.relation.ispartofseries | Communications in Computer and Information Science ; 723 | - |
dc.subject | Deep neural network | - |
dc.subject | White matter hyperintensities | - |
dc.subject | Segmentation | - |
dc.subject | Supervised learning | - |
dc.subject | Brain MRI | - |
dc.title | Evaluation of four supervised learning schemes in white matter hyperintensities segmentation in absence or mild presence of vascular pathology | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-60964-5_42 | - |
dc.identifier.scopus | eid_2-s2.0-85022181177 | - |
dc.identifier.spage | 482 | - |
dc.identifier.epage | 493 | - |
dc.identifier.eissn | 1865-0937 | - |
dc.identifier.isi | WOS:000770548800042 | - |
dc.publisher.place | Cham, Switzerland | - |
dc.identifier.issnl | 1865-0929 | - |