Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

PeerJ

article

Wieslaw L. Nowinski¹  Jerzy Walecki²  Gabriela Półtorak-Szymczak²  Katarzyna Sklinda²  Bartosz Mruk² 

[1] John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski;Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education

关键词: Ischemic stroke;    Human brain;    Detection;    Localization;    Segmentation;    Noncontrast CT;    Brain atlas;    Image processing;    Image analysis;    Artificial intelligence;    Review;   

DOI  :  10.7717/peerj.10444

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.

【 授权许可】

CC BY