A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.
Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automat...
| Publicado en: | Neuroradiology Vol. 64; no. 4; pp. 727 - 735 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155691303&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155691303 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Apr2022 vid: 64 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155691303 152734499 155691303 155691303 10.1007/s00234-021-02820-w 155691303 ppf: 727 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep learning algorithm for white matter hyperintensity lesion detection and segmentation. aug: au: Zhang, Yajing Duan, Yunyun Wang, Xiaoyang Zhuo, Zhizheng Haller, Sven Barkhof, Frederik Liu, Yaou affil: MR Clinical Science, Philips Healthcare, 258 Zhongyuan Rd, Suzhou, SIP, China sug: subj: Deep Learning Algorithms White Matter Pathology Image Processing, Computer Assisted Methods Multiple Sclerosis Human Magnetic Resonance Imaging Prospective Studies Neural Networks (Computer) Funding Source Workflow Sensitivity and Specificity ab: Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. Methods: We developed and evaluated "DeepWML", a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). Results: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool's performance increased with larger lesion volumes. Conclusion: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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