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
- (2019) Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Transactions on Medical Imaging.
- (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. vol. 11045 LNCS.
- (2017) MIIP: A web-based platform for medical image interpretation training and evaluation focusing on ultrasound. Progress in Biomedical Optics and Imaging. vol. 10138.
- (2017) Automatic Segmentation and Probe Guidance for Real-Time Assistance of Ultrasound-Guided Femoral Nerve Blocks. Ultrasound in Medicine and Biology. vol. 43 (1).
- (2017) 2D left ventricle segmentation using deep learning. Proceedings - IEEE Ultrasonics Symposium.
- (2017) Real-time classification of standard cardiac views in echocardiography using neural networks. Proceedings - IEEE Ultrasonics Symposium.
- (2016) Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography. IEEE Transactions on Medical Imaging. vol. 35 (4).
- (2016) Reconstruction of In Vivo Flow Velocity Fields Based On a Rapid Ultrasound Image Segmentation and B-spline Regularization Framework. Proceedings - IEEE Ultrasonics Symposium. vol. 2016-November.
- (2016) Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve. IEEE Transactions on Medical Imaging. vol. 35 (3).
- (2016) Vessel detection in ultrasound images using deep convolutional neural networks. Lecture Notes in Computer Science. vol. 10008 LNCS.
- (2015) Airway segmentation and centerline extraction from thoracic CT - Comparison of a new method to state of the art commercialized methods. PLOS ONE. vol. 10:e0144282 (12).
- (2015) FAST: framework for heterogeneous medical image computing and visualization. International Journal of Computer Assisted Radiology and Surgery. vol. 10 (11).
- (2015) Real-time gradient vector flow on GPUs using OpenCL. Journal of Real-Time Image Processing. vol. 10 (1).