Publication list

Thesis

Articles

Below is a list of research articles I have contributed to.

Title Authors Published in Download article Code
Tracking-based mitral annular plane systolic excursion (MAPSE) measurement using deep learning in B-mode ultrasound Erik Smistad, Andreas Østvik, Jahn Frederik Grue, Håvard Dalen, Lasse Løvstakken Proceedings of IEEE International Ultrasonics Symposium (IUS). 2022. Download
Segmentation of parasternal long axis views using deep learning Erik Smistad, Håvard Dalen, Bjørnar Grenne, Lasse Løvstakken Proceedings of IEEE International Ultrasonics Symposium (IUS). 2022. Download
Real-time temporal coherent left ventricle segmentation using convolutional LSTMs Erik Smistad, Ivar Mjåland Salte, Håvard Dalen, Lasse Løvstakken Proceedings of IEEE International Ultrasonics Symposium (IUS). 2021. Download
Real-time 3D left ventricle segmentation and ejection fraction using deep learning Erik Smistad, Erik Nikolai Steinsland, Lasse Løvstakken Proceedings of IEEE International Ultrasonics Symposium (IUS). 2021. Download
Annotation Web – An open-source web-based annotation tool for ultrasound images Erik Smistad, Andreas Østvik, Lasse Løvstakken Proceedings of IEEE International Ultrasonics Symposium (IUS). 2021. Download
Real-time segmentation of blood vessels, nerves and bone in ultrasound-guided regional anesthesia using deep learning Erik Smistad, Torgrim Lie, Kaj Fredrik Johansen Proceedings of IEEE International Ultrasonics Symposium (IUS). 2021. Download
Real-time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning Erik Smistad, Andreas Østvik, Ivar Mjåland Salte, Daniela Melichova, Thuy Mi Nguyen, Kristina Haugaa, Harald Brunvand, Thor Edvardsen, Sarah Leclerc, Olivier Bernard, Bjørnar Grenne, Lasse Løvstakken IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. 2020 Download open access article here
High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST Erik Smistad, Andreas Østvik and André Pedersen. IEEE Access. Volume 7. 2019. Download open access article here Code @ GitHub
Segmentation of apical long axis, four- and two-chamber views using deep neural networks Erik Smistad, Ivar Mjåland Salte, Andreas Østvik, Sarah Leclerc, Olivier Bernard and Lasse Løvstakken. Proceedings of IEEE International Ultrasonics Symposium (IUS), Glasgow 6-10 Oct. 2019. Download preprint here
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography Sarah Leclerc, Erik Smistad, João Pedrosa, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier, Carole Lartizien, Jan D’hooge, Lasse Lovstakken, Olivier Bernard. IEEE Transactions on Medical Imaging. 2019. Download final article here: https://doi.org/10.1109/TMI.2019.2900516
Highlighting nerves and blood vessels for ultrasound-guided axillary nerve block procedures using neural networks Erik Smistad, Kaj Fredrik Johansen, Daniel Høyer Iversen, Ingerid Reinertsen Journal of Medical Imaging. Volume 5, Issue 4. 2018. Download postprint article. Copyright 2018 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.

https://doi.org/10.1117/1.JMI.5.4.044004

Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks Erik Smistad, Andreas Østvik, Ivar Mjåland Salte, Sarah Leclerc, Olivier Bernard and Lasse Løvstakken Proceedings of International Ultrasonics Symposium (IUS), Kobe 22-25 Oct. 2018. Download preprint article. The final publication is available at IEEE via https://doi.org/10.1109/ULTSYM.2018.8579886
Ultrasound speckle reduction using generative adversial networks Fabian Dietrichson, Erik Smistad, Andreas Østvik and Lasse Løvstakken Proceedings of International Ultrasonics Symposium (IUS), Kobe 22-25 Oct. 2018. Download preprint article. The final publication is available at IEEE via https://doi.org/10.1109/ULTSYM.2018.8579764
Detection of Cardiac Events in Echocardiography using 3D Convolutional Recurrent Neural Networks Adrian Meidell Fiorito, Andreas Østvik, Erik Smistad, Sarah Leclerc, Olivier Bernard and Lasse Løvstakken Proceedings of International Ultrasonics Symposium (IUS), Kobe 22-25 Oct. 2018. Download preprint article. The final publication is available at IEEE via https://doi.org/10.1109/ULTSYM.2018.8580137
Deep learning applied to multi-structure segmentation in 2D echocardiography: a preliminary investigation of the required database size Sarah Leclerc, Erik Smistad, Thomas Grenier, Carole Lartizien, Andreas Østvik, Florian Espinosa, Pierre-Marc Jodoin, Lasse Lovstakken, Olivier Bernard. Proceedings of International Ultrasonics Symposium (IUS), Kobe 22-25 Oct. 2018 The final publication is available at IEEE via https://doi.org/10.1109/ULTSYM.2018.8580136
Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks Andreas Østvik, Erik Smistad, Svein Arne Aase, Bjørn Olav Haugen, Lasse Lovstakken Ultrasound in Medicine and Biology. 2018. Final publication available at Elsevier
Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning Andreas Østvik, Erik Smistad, Torvald Espeland, Erik Andreas Rye Berg and Lasse Løvstakken Proceedings of the 4th Workshop on Deep Learning in Medical Image Analysis (DLMIA). Granada, 20. Sept. 2018. Pages 309-316 The final publication is available at Springer via https://doi.org/10.1007/978-3-030-00889-5_35
2D left ventricle segmentation using deep learning Erik Smistad, Andreas Østvik, Bjørn Olav Haugen and Lasse Løvstakken Proceedings of International Ultrasonics Symposium (IUS), Washington DC 6-9 Sept. 2017. Download preprint article. The final publication is available at IEEE via http://dx.doi.org/10.1109/ULTSYM.2017.8092573
Vessel detection in ultrasound images using deep convolutional neural networks Erik Smistad and Lasse Løvstakken Proceedings of the 2nd Workshop on Deep Learning in Medical Image Analysis (DLMIA). Athens, 21. Oct. 2016. Pages 30-38 Download preprint article. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46976-8_4
Reconstruction of in vivo flow velocity fields based on a rapid ultrasound image segmentation and B-spline regularization framework Thomas Grønli, Erik Smistad, Siri Ann Nyrnes, Alberto Gomez, Lasse Løvstakken Proceedings of International Ultrasonics Symposium (IUS), Tours 18-21 Sept. 2016. Final publication available at IEEE
Automatic segmentation and probe guidance for real-time assistance of ultrasound-guided femoral nerve blocks Erik Smistad, Daniel H. Iversen, Linda Leidig, Janne Beate Lervik Bakeng, Kaj Fredrik Johansen and Frank Lindseth Ultrasound in Medicine and Biology. Volume 43, Issue 1, pages 218-226, 2017. Download postprint
Final publication available at Elsevier
Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve Erik Smistad and Frank Lindseth IEEE Transactions on Medical Imaging. Volume 35, Issue 3, pages 752-761, 2016. Download article (Open access)
Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography O Bernard, J Bosch, B Heyde, M Alessandrini, D Barbosa, S Camarasu-Pop, F Cervenansky, S Valette, O Mirea, M Bernier, P Jodoin, J Domingos, R Stebbing, K Keraudren, O Oktay, J Caballero, W Shi, D Rueckert, F Milletari, S Ahmadi, E Smistad, F Lindseth, M van Stralen, C Wang, O Smedby, E Donal, M Monaghan, A Papachristidis, M Geleijnse, E Galli, J D’hooge IEEE Transactions on Medical Imaging, Volume 35, Issue 4, pages 967-977, 2016. Final publication available at IEEE
Airway Segmentation and Centerline Extraction from Thoracic CT-Comparison of a New Method to State of the Art Commercialized Methods Pall Jens Reynisson, Marta Scali, Erik Smistad, Erlend Fagertun Hofstad, Håkon Olav Leira, Frank Lindseth, Toril AnitaNagelhus Hernes, Tore Amundsen, Hanne Sorger, Thomas Langø PLoS One. Volume 10, Issue 12, 2015 Download article (Open access) Code @ GitHub
FAST: framework for heterogeneous medical image computing and visualization Erik Smistad, Mohammadmehdi Bozorgi and Frank Lindseth International Journal of Computer Assisted Radiology and Surgery. Volume 10, Issue 11, pages 1811-1822, 2015. Download preprint article.
The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-015-1158-5
Code @ GitHub
Medical image segmentation on GPUs – A comprehensive review Erik Smistad, Thomas L. Falch, Mohammadmehdi Bozorgi, Anne C. Elster and Frank Lindseth Medical Image Analysis. Volume 20, pages 1-18, February 2015 Download article (Open access).
Real-time Tracking of the Left Ventricle in 3D Ultrasound Using Kalman Filter and Mean Value Coordinates Erik Smistad and Frank Lindseth Proceedings MICCAI Challenge on Echocardiographic Three-Dimensional Ultrasound
Segmentation (CETUS), Boston, Sept. 14, 2014, pp. 65-72.
Download article (Open access). Coming
A New Tube Detection Filter for Abdominal Aortic Aneurysms Erik Smistad, Reidar Brekken and Frank Lindseth Proceedings of MICCAI 2014 Workshop on Abdominal Imaging: Computational and Clinical Applications. Boston, Sept. 14, 2014. Download preprint article.
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13692-9_22
Code @ GitHub
Multigrid gradient vector flow computation on the GPU Erik Smistad and Frank Lindseth Journal of Real-Time Image Processing. October 2014. Download preprint article.
The final publication is available at Springer via http://dx.doi.org/10.1007/s11554-014-0466-2
Code @ GitHub
GPU Accelerated Segmentation and Centerline Extraction of Tubular Structures from Medical Images Erik Smistad, Anne C. Elster and Frank Lindseth International Journal of Computer Assisted Radiology and Surgery. July 2014, Volume 9, Issue 4, pp 561-575 Download preprint article. The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-013-0956-x Code @ GitHub
Real-time gradient vector flow on GPUs using OpenCL Erik Smistad, Anne C. Elster and Frank Lindseth Journal of Real-Time Image Processing. March 2015, Volume 10, Issue 1, pp 67-74 Download preprint article. The final publication is available at Springer via http://dx.doi.org/10.1007/s11554-012-0257-6 Code @ GitHub
GPU-Based Airway Segmentation and Centerline Extraction Erik Smistad, Anne C. Elster and Frank Lindseth Norsk informatikkonferanse 2012. pp 129-140 Download article (Open access). Code @ GitHub
Real-time surface extraction and visualization of medical images using OpenCL and GPUs Erik Smistad, Anne C. Elster and Frank Lindseth Norsk informatikkonferanse 2012. pp 141-152 Download article (Open access). Code @ GitHub