File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Deep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images

TitleDeep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images
Authors
KeywordsWhite matter hyperintensities
Neuroimaging
MRI
Segmentation
Localization
Issue Date2020
PublisherSpringer, co-published with International Association of Scientists in the Interdisciplinary Areas. The Journal's web site is located at https://www.springer.com/journal/12539
Citation
Interdisciplinary Sciences: Computational Life Sciences, 2020, v. 12 n. 4, p. 438-446 How to Cite?
AbstractWhite matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual or semi-automatic delineation and intuitive localization, which is time-consuming and observer-dependent. Current automatic quantification methods focus mainly on the segmentation, but the spatial distribution of lesions plays a vital role in clinical diagnosis. In this study, we implemented four segmentation algorithms and compared the performances quantitatively and qualitatively on two open-access datasets. The location-specific analysis was conducted sequentially on 213 clinical patients with cerebral ischemia and lacune. The experimental results suggest that our deep-learning-based model has the potential to be integrated into the clinical workflow.
Persistent Identifierhttp://hdl.handle.net/10722/306379
ISSN
2023 Impact Factor: 3.9
2023 SCImago Journal Rankings: 0.694
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, W-
dc.contributor.authorLin, F-
dc.contributor.authorZhang, J-
dc.contributor.authorZhan, T-
dc.contributor.authorCao, P-
dc.contributor.authorWang, S-
dc.date.accessioned2021-10-20T10:22:46Z-
dc.date.available2021-10-20T10:22:46Z-
dc.date.issued2020-
dc.identifier.citationInterdisciplinary Sciences: Computational Life Sciences, 2020, v. 12 n. 4, p. 438-446-
dc.identifier.issn1913-2751-
dc.identifier.urihttp://hdl.handle.net/10722/306379-
dc.description.abstractWhite matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual or semi-automatic delineation and intuitive localization, which is time-consuming and observer-dependent. Current automatic quantification methods focus mainly on the segmentation, but the spatial distribution of lesions plays a vital role in clinical diagnosis. In this study, we implemented four segmentation algorithms and compared the performances quantitatively and qualitatively on two open-access datasets. The location-specific analysis was conducted sequentially on 213 clinical patients with cerebral ischemia and lacune. The experimental results suggest that our deep-learning-based model has the potential to be integrated into the clinical workflow.-
dc.languageeng-
dc.publisherSpringer, co-published with International Association of Scientists in the Interdisciplinary Areas. The Journal's web site is located at https://www.springer.com/journal/12539-
dc.relation.ispartofInterdisciplinary Sciences: Computational Life Sciences-
dc.subjectWhite matter hyperintensities-
dc.subjectNeuroimaging-
dc.subjectMRI-
dc.subjectSegmentation-
dc.subjectLocalization-
dc.titleDeep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images-
dc.typeArticle-
dc.identifier.emailCao, P: caopeng1@hku.hk-
dc.identifier.authorityCao, P=rp02474-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s12539-020-00398-0-
dc.identifier.pmid33140170-
dc.identifier.scopuseid_2-s2.0-85094914082-
dc.identifier.hkuros326788-
dc.identifier.volume12-
dc.identifier.issue4-
dc.identifier.spage438-
dc.identifier.epage446-
dc.identifier.isiWOS:000584437100001-
dc.publisher.placeCanada-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats