End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

Journal: Neuroradiology
PMID:

Abstract

PURPOSE: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.

Authors

  • İlker Özgür Koska
    Department of Radiology, Behçet Uz Children's Hospital, İzmir, Turkey.
  • Alper Selver
    İzmir Health Technologies Development and Accelerator (BioIzmir), Dokuz Eylül University, İzmir, Turkey.
  • Fazıl Gelal
    Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Basin Sitesi, Izmir, 35360, Turkey.
  • Muhsın Engın Uluc
    Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Basin Sitesi, Izmir, 35360, Turkey.
  • Yusuf Kenan Cetinoglu
    Batman Training and Research Hospital, Department of Radiology, 72070 Batman, Turkey. Electronic address: kenancetinoglu@hotmail.com.
  • Nursel Yurttutan
    Department of Radiology, Kahramanmaraş Sütçü İmam University Hospital, Kahramanmaraş, Turkey.
  • Mehmet Serındere
    Department of Radiology, Hatay Training and Research Hospital, Güzelburç/ Hatay, Turkey.
  • Oğuz Dicle
    Department of Radiology, Dokuz Eylül University School of Medicine, İzmir, Turkey.