Acute ischemic stroke and intracranial hemorrhages (ICH) represent critical situations for the patient. Rapid accurate diagnosis and therapy are required to prevent serious lifelong consequences or death. In the case of suspected head circulatory pathology, computed tomography (CT) is often the first choice among imaging techniques because of its availability, speed and reliability. In order to refine and speed up the diagnostic process, advanced algorithms implemented in computer aided diagnosis systems are currently being developed. This paper presents approaches to an automatic ICH localization as a part of a research project aimed at the development of machine learning methods for the analysis of circulatory disorders in head CT scans. Three designed deep learning-based algorithms are described and compared for prediction of the exact position of ICH within a 3D CT scan, and in two cases also for classification into the sub-types. An objective evaluation of the methods is presented along with a discussion of the results. Further possibilities for circulatory diseases analysis in head CT scans using modern algorithms are also discussed.

Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

References provided by Crossref.org

Literatura