BACKGROUND AND PURPOSE: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary to determine eligibility for late-time-window thrombectomy. Therefore, the aim of ISLES-2018 was to segment infarcted tissue on CTP based on diffusion-weighted imaging as a reference standard. METHODS: The data, from 4 centers, consisted of 103 cases of acute anterior circulation large artery occlusion stroke who underwent diffusion-weighted imaging rapidly after CTP. Diffusion-weighted imaging lesion segmentation was performed manually and acted ...
Peer reviewed: TrueBackground:: In ischaemic stroke patients undergoing reperfusion therapy, the amo...
Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describe...
International audienceMachine Learning (ML) has been proposed for tissue fate prediction after acute...
BACKGROUND AND PURPOSE The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally...
Introduction: Imaging studies are used to guide patient selection for acute stroke treatment. Perfus...
Objective: Computed tomography (CT) scan is a fast and widely used modality for early assessment in ...
IntroductionComputed tomography perfusion (CTP) imaging is widely used in cases of suspected acute i...
Background and Purpose: Prediction of infarct extent among patients with acute ischemic stroke using...
BACKGROUND AND PURPOSE: Computed tomography perfusion imaging allows estimation of tissue status in ...
(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stro...
Performance of models highly depend not only on the used algorithm but also the data set it was appl...
(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER...
PhD thesis in Information technologyThis thesis investigates artificial intelligence (AI) methodolog...
Stroke is the fifth leading cause of death in the United States, with approximately 795,000 new case...
Stroke is the second most common cause of death in developed countries. Rapid clinical assessment an...
Peer reviewed: TrueBackground:: In ischaemic stroke patients undergoing reperfusion therapy, the amo...
Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describe...
International audienceMachine Learning (ML) has been proposed for tissue fate prediction after acute...
BACKGROUND AND PURPOSE The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally...
Introduction: Imaging studies are used to guide patient selection for acute stroke treatment. Perfus...
Objective: Computed tomography (CT) scan is a fast and widely used modality for early assessment in ...
IntroductionComputed tomography perfusion (CTP) imaging is widely used in cases of suspected acute i...
Background and Purpose: Prediction of infarct extent among patients with acute ischemic stroke using...
BACKGROUND AND PURPOSE: Computed tomography perfusion imaging allows estimation of tissue status in ...
(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stro...
Performance of models highly depend not only on the used algorithm but also the data set it was appl...
(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER...
PhD thesis in Information technologyThis thesis investigates artificial intelligence (AI) methodolog...
Stroke is the fifth leading cause of death in the United States, with approximately 795,000 new case...
Stroke is the second most common cause of death in developed countries. Rapid clinical assessment an...
Peer reviewed: TrueBackground:: In ischaemic stroke patients undergoing reperfusion therapy, the amo...
Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describe...
International audienceMachine Learning (ML) has been proposed for tissue fate prediction after acute...