Evaluating severity of white matter lesions from computed tomography images with convolutional neural network.
Purpose: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional...
| Published in: | Neuroradiology Vol. 62; no. 10; pp. 1257 - 1264 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145626121&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145626121 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2020 vid: 62 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145626121 143998772 145626121 145626121 10.1007/s00234-020-02410-2 145626121 ppf: 1257 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluating severity of white matter lesions from computed tomography images with convolutional neural network. aug: au: Pitkänen, Johanna Koikkalainen, Juha Nieminen, Tuomas Marinkovic, Ivan Curtze, Sami Sibolt, Gerli Jokinen, Hanna Rueckert, Daniel Barkhof, Frederik Schmidt, Reinhold Pantoni, Leonardo Scheltens, Philip Wahlund, Lars-Olof Korvenoja, Antti Lötjönen, Jyrki Erkinjuntti, Timo Melkas, Susanna affil: Department of Neurology, University of Helsinki and Helsinki University Hospital, PO Box 302, 00029 HUS, Helsinki, Finland sug: subj: White Matter Physiopathology White Matter Radiography Neural Networks (Computer) Utilization Tomography, X-Ray Computed Human Brain Physiopathology Magnetic Resonance Imaging Image Processing, Computer Assisted Machine Learning Pearson's Correlation Coefficient ab: Purpose: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods: The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results: A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion: CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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