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000195012 037__ $$aDZNE-2023-00191
000195012 041__ $$aEnglish
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000195012 1001_ $$0P:(DE-2719)2814314$$aGaubert, Malo$$b0$$eFirst author
000195012 245__ $$aPerformance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia
000195012 260__ $$aLausanne$$bFrontiers Research Foundation$$c2023
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000195012 500__ $$aThe DELCODE study was funded by the German Center for Neurodegenerative Diseases (reference no. BN012). The DELCODE study was supported by the Max-Delbrück-centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Freie Universität Berlin Center for Cognitive Neuroscience Berlin (CCNB), Nuklearmedizin und Klinische Molekulare Bildgebung—Univeristätsklinikum Tübingen Bernstein Center für Computional Neuroscience Berlin, Universitätsmedizin Göttingen Core Facility MR-Research Göttingen, Institut für Klinische Radiologie Klinikum der Universität München, and Universitätsklinikum Tübingen MR-Forschungszentrum.
000195012 520__ $$aWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer's disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice's coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.
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000195012 536__ $$0G:(DE-HGF)POF4-352$$a352 - Disease Mechanisms (POF4-352)$$cPOF4-352$$fPOF IV$$x1
000195012 536__ $$0G:(DE-HGF)POF4-351$$a351 - Brain Function (POF4-351)$$cPOF4-351$$fPOF IV$$x2
000195012 588__ $$aDataset connected to CrossRef, Journals: pub.dzne.de
000195012 650_7 $$2Other$$aAlzheimer’s disease
000195012 650_7 $$2Other$$aAlzheimer’s disease
000195012 650_7 $$2Other$$aFLAIR
000195012 650_7 $$2Other$$aaging
000195012 650_7 $$2Other$$adeep learning
000195012 650_7 $$2Other$$aevaluation
000195012 650_7 $$2Other$$awhite matter hyperintensities segmentation
000195012 693__ $$0EXP:(DE-2719)DELCODE-20140101$$5EXP:(DE-2719)DELCODE-20140101$$eLongitudinal Cognitive Impairment and Dementia Study$$x0
000195012 7001_ $$0P:(DE-2719)9001684$$aDell Orco, Andrea$$b1$$udzne
000195012 7001_ $$0P:(DE-2719)9000635$$aLange, Catharina$$b2
000195012 7001_ $$0P:(DE-HGF)0$$aGarnier-Crussard, Antoine$$b3
000195012 7001_ $$0P:(DE-2719)9000833$$aZimmermann, Isabella$$b4
000195012 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b5
000195012 7001_ $$0P:(DE-HGF)0$$aDuering, Marco$$b6
000195012 7001_ $$0P:(DE-2719)2814076$$aZiegler, Gabriel$$b7
000195012 7001_ $$0P:(DE-2719)2811024$$aPeters, Oliver$$b8
000195012 7001_ $$0P:(DE-2719)9000703$$aPreis, Lukas$$b9
000195012 7001_ $$0P:(DE-2719)2811122$$aPriller, Josef$$b10
000195012 7001_ $$0P:(DE-2719)2812446$$aSpruth, Eike Jakob$$b11
000195012 7001_ $$0P:(DE-2719)2812035$$aSchneider, Anja$$b12
000195012 7001_ $$0P:(DE-2719)2811326$$aFliessbach, Klaus$$b13
000195012 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b14
000195012 7001_ $$0P:(DE-2719)2814326$$aSchott, Björn H.$$b15
000195012 7001_ $$0P:(DE-HGF)0$$aMaier, Franziska$$b16
000195012 7001_ $$0P:(DE-2719)2811614$$aGlanz, Wenzel$$b17
000195012 7001_ $$0P:(DE-2719)2811351$$aBuerger, Katharina$$b18
000195012 7001_ $$0P:(DE-2719)9002557$$aJanowitz, Daniel$$b19
000195012 7001_ $$0P:(DE-2719)2812234$$aPerneczky, Robert$$b20
000195012 7001_ $$0P:(DE-2719)9001808$$aRauchmann, Boris-Stephan$$b21
000195012 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan$$b22
000195012 7001_ $$0P:(DE-2719)2810394$$aKilimann, Ingo$$b23
000195012 7001_ $$0P:(DE-2719)2000055$$aLaske, Christoph$$b24
000195012 7001_ $$0P:(DE-2719)9001516$$aMunk, Matthias H.$$b25
000195012 7001_ $$0P:(DE-2719)2811324$$aSpottke, Annika$$b26
000195012 7001_ $$0P:(DE-2719)2812341$$aRoy, Nina$$b27
000195012 7001_ $$0P:(DE-2719)2811611$$aDobisch, Laura$$b28
000195012 7001_ $$0P:(DE-2719)9000543$$aEwers, Michael$$b29
000195012 7001_ $$0P:(DE-HGF)0$$aDechent, Peter$$b30
000195012 7001_ $$0P:(DE-HGF)0$$aHaynes, John Dylan$$b31
000195012 7001_ $$0P:(DE-HGF)0$$aScheffler, Klaus$$b32
000195012 7001_ $$0P:(DE-2719)2000005$$aDüzel, Emrah$$b33
000195012 7001_ $$0P:(DE-2719)2000032$$aJessen, Frank$$b34
000195012 7001_ $$0P:(DE-2719)2814122$$aWirth, Miranka$$b35$$eLast author
000195012 773__ $$0PERI:(DE-600)2564218-2$$a10.3389/fpsyt.2022.1010273$$gVol. 13, p. 1010273$$p1010273$$tFrontiers in psychiatry$$v13$$x1664-0640$$y2023
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