Andreas Østvik
Background and activities
Research Scientist at the Department of Circulation and Medical Imaging financed by CIUS. Current research projects involve automatic methods for analysing medical images using machine learning methodology. The main application is cardiac ultrasound imaging, where the goal is to develop good diagnostic tools and improve workflow in the clinic.
Research interests
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Ultrasound, and other medical imaging modalities
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Machine learning with emphasis on deep neural networks
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Robotics and machine vision in medical intervention
Background
Master of Science (MSc) in physics and mathematics with specialization in biophysics and medical technology. Master thesis on the topic of Robot Control in Image-Guided Intervention. PhD within automatic analysis in echocardiography using machine learning.
Also working as a Research Scientist at SINTEF Digital, Department of Health Research.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
Journal publications
- (2021) Can a Dinosaur Think? Implementation of Artificial Intelligence in Extracorporeal Shock Wave Lithotripsy. European Urology Open Science. vol. 27.
- (2021) Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovascular Imaging.
- (2021) Myocardial Function Imaging in Echocardiography Using Deep Learning. IEEE Transactions on Medical Imaging. vol. 40 (5).
- (2020) LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. vol. 67 (12).
- (2020) Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. vol. 67 (12).
- (2019) RU-Net: A refining segmentation network for 2D echocardiography. Proceedings - IEEE Ultrasonics Symposium. vol. 2019-October.
- (2019) Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Transactions on Medical Imaging. vol. 38 (9).
- (2019) Segmentation of apical long axis, four- and two-chamber views using deep neural networks. Proceedings - IEEE Ultrasonics Symposium. vol. 2019-October.
- (2019) High performance neural network inference, streaming, and visualization of medical images using FAST. IEEE Access. vol. 7.
- (2018) Ultrasound speckle reduction using generative adversial networks. Proceedings - IEEE Ultrasonics Symposium. vol. 2018-October:8579764.
- (2018) Detection of Cardiac Events in Echocardiography using 3D Convolutional Recurrent Neural Networks. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Deep learning applied to multi-structure segmentation in 2D echocardiography: A preliminary investigation of the required database size. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks. Ultrasound in Medicine and Biology.
- (2018) Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning. Lecture Notes in Computer Science (LNCS). vol. 11045 LNCS.
- (2017) 2D left ventricle segmentation using deep learning. Proceedings - IEEE Ultrasonics Symposium.