Erik Smistad
Background and activities
Working 50% as a post doc at the CIUS project and 50% as a researcher at SINTEF Medical Technology.
Primary research interests
- Image segmentation
- Machine learning and neural networks
- Parallel and GPU processing
- Ultrasound
If you are interested in the same topics, please don't hesitate to contact me at erik.smistad@ntnu.no.
See my personal webpage www.eriksmistad.no for more information about my research.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2021) FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology. IEEE Access. vol. 9.
- (2021) Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovascular Imaging. vol. 14 (10).
- (2021) Annotation Web - An open-source web-based annotation tool for ultrasound images. Proceedings - IEEE Ultrasonics Symposium.
- (2021) Real-time segmentation of blood vessels, nerves and bone in ultrasound-guided regional anesthesia using deep learning. Proceedings - IEEE Ultrasonics Symposium.
- (2021) Real-time temporal coherent left ventricle segmentation using convolutional LSTMs. Proceedings - IEEE Ultrasonics Symposium.
- (2021) Real-time 3D left ventricle segmentation and ejection fraction using deep learning. Proceedings - IEEE Ultrasonics Symposium.
- (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) Highlighting nerves and blood vessels for ultrasound guided axillary nerve block procedures using neural networks. Journal of Medical Imaging. vol. 5 (4).
- (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.