Picture for Aad van der Lugt

Aad van der Lugt

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands

An automated framework for brain vessel centerline extraction from CTA images

Add code
Jan 13, 2024
Viaarxiv icon

AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography

Add code
Oct 09, 2023
Viaarxiv icon

Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

Aug 03, 2022
Figure 1 for Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography
Figure 2 for Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography
Figure 3 for Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography
Figure 4 for Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography
Viaarxiv icon

A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Add code
Sep 22, 2021
Figure 1 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Figure 2 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Figure 3 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Figure 4 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Viaarxiv icon

Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease

Add code
Dec 16, 2020
Figure 1 for Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Figure 2 for Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Figure 3 for Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Figure 4 for Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Viaarxiv icon

autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients

Oct 06, 2020
Figure 1 for autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Figure 2 for autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Figure 3 for autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Figure 4 for autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Viaarxiv icon

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

Dec 06, 2018
Figure 1 for Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Figure 2 for Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Figure 3 for Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Figure 4 for Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Viaarxiv icon

Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks

Jun 04, 2017
Figure 1 for Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Figure 2 for Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Figure 3 for Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Figure 4 for Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Viaarxiv icon