title: Segmentation of Hyperacute Cerebral Infarcts Based on Sparse Representation of Diffusion Weighted Imaging.
abstract: Segmentation of infarcts at hyperacute stage is challenging as they exhibit substantial variability which may even be hard for experts to delineate manually. In this paper, a sparse representation based classification method is explored. For each patient, four volumetric data items including three volumes of diffusion weighted imaging and a computed asymmetry map are employed to extract patch features which are then fed to dictionary learning and classification based on sparse representation. Elastic net is adopted to replace the traditionalL0-norm/L1-norm constraints on sparse representation to stabilize sparse code. To decrease computation cost and to reduce false positives, regions-of-interest are determined to confine candidate infarct voxels. The proposed method has been validated on 98 consecutive patients recruited within 6 hours from onset. It is shown that the proposed method could handle well infarcts with intensity variability and ill-defined edges to yield significantly higher Dice coefficient (0.755 ± 0.118) than the other two methods and their enhanced versions by confining their segmentations within the regions-of-interest (average Dice coefficient less than 0.610). The proposed method could provide a potential tool to quantify infarcts from diffusion weighted imaging at hyperacute stage with accuracy and speed to assist the decision making especially for thrombolytic therapy.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Jing
authors.affiliation: Beijing Tiantan Hospital, Capital Medical University, 6 Tiantan Xili, Beijing 100050, China.
authors.forename: Peiyi
authors.initials: P
authors.lastname: Gao
authors.affiliation: Beijing Tiantan Hospital, Capital Medical University, 6 Tiantan Xili, Beijing 100050, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xue
authors.affiliation: Beijing Tiantan Hospital, Capital Medical University, 6 Tiantan Xili, Beijing 100050, China.
authors.forename: Lu
authors.initials: L
authors.lastname: Su
authors.affiliation: Shenzhen Second People's Hospital, 3002 West Sungang Road, Shenzhen 518035, China.
authors.forename: Weiping
authors.initials: W
authors.lastname: Li
authors.affiliation: Shenzhen Second People's Hospital, 3002 West Sungang Road, Shenzhen 518035, China.
authors.forename: Lijie
authors.initials: L
authors.lastname: Ren
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China.
authors.forename: Qingmao
authors.identifier: 0000-0002-2465-0556
authors.initials: Q
authors.lastname: Hu
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging; D002544:Cerebral Infarction / Q000000981:diagnostic imaging / Q000503:physiopathology*; D003657:Decision Making; D038524:Diffusion Magnetic Resonance Imaging; D016002:Discriminant Analysis; D004548:Elasticity; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012042:Registries; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D020521:Stroke / Q000000981:diagnostic imaging* / Q000503:physiopathology; D015912:Thrombolytic Therapy