Matthias Ivantsits

Orcid: 0000-0003-0317-7154

Affiliations:
  • Charité, Berlin, Germany


According to our database1, Matthias Ivantsits authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+t echocardiography.
Comput. Biol. Medicine, 2024

2023
Deep Learning-Based Pulmonary Artery Surface Mesh Generation.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, 2023

Matching Endoscopic 3D Image Data with 4D Echocardiographic Data for Extended Reality Support in Mitral Valve Repair Surgery.
Proceedings of the Bildverarbeitung für die Medizin 2023, 2023

2022
Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.
Medical Image Anal., 2022

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge.
Medical Image Anal., 2022

3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, 2022

2020
Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection.
Proceedings of the Cerebral Aneurysm Detection - First Challenge, 2020

Deep-Learning-Based Myocardial Pathology Detection.
Proceedings of the Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2020

Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning.
Proceedings of the Cerebral Aneurysm Detection - First Challenge, 2020

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA).
Proceedings of the Cerebral Aneurysm Detection - First Challenge, 2020

Intracranial Aneurysm Rupture Prediction with Computational Fluid Dynamics Point Clouds.
Proceedings of the Cerebral Aneurysm Detection - First Challenge, 2020

Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI.
Proceedings of the Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2020


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