Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation
| Autor: | Didier Dormont, Thomas Samaille, Olivier Colliot, Hugues Chabriat, Marie Chupin, Rémi Cuingnet, Ludovic Fillon, Eric Jouvent |
|---|---|
| Přispěvatelé: | Algorithms, models and methods for images and signals of the human brain (ARAMIS), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), MedisysResearch Lab (Medisys), Philips Research, Metacohorts Consortium, DHU Neurovasculaire, Service de Neuroradiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Neurosciences cognitives et imagerie cérébrale (NCIC), Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Colliot, Olivier, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP) |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Intraclass correlation
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Image Processing Cerebrovascular Diseases lcsh:Medicine Neuroimaging Fluid-attenuated inversion recovery Biology Bioinformatics 030218 nuclear medicine & medical imaging Diagnostic Radiology 03 medical and health sciences 0302 clinical medicine Engineering [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Neurobiology of Disease and Regeneration Image Processing Computer-Assisted Preprocessor Humans Segmentation lcsh:Science [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing Internet Multidisciplinary business.industry lcsh:R Contrast (statistics) Brain Reproducibility of Results Pattern recognition Thresholding Magnetic Resonance Imaging Hyperintensity Support vector machine Neurology Computer Science Signal Processing Medicine lcsh:Q Artificial intelligence business Radiology [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 030217 neurology & neurosurgery Software Algorithms Research Article Neuroscience |
| Zdroj: | PLoS ONE, Vol 7, Iss 11, p e48953 (2012) PLoS ONE PLoS ONE, Public Library of Science, 2012, 7 (11), pp.e48953. ⟨10.1371/journal.pone.0048953⟩ PLoS ONE, 2012, 7 (11), pp.e48953. ⟨10.1371/journal.pone.0048953⟩ |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0048953⟩ |
| Popis: | International audience; White matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM), and did not need any training set. |
| Databáze: | OpenAIRE |
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