Erik Smistad

发表

LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography pdf

Frederic Cervenansky, Carole Lartizien, Olivier Bernard, 2020, ArXiv.

Reconstruction of in vivo flow velocity fields based on a rapid ultrasound image segmentation and B-spline regularization framework

Lasse Lovstakken, Erik Smistad, Thomas Gronli, 2016, 2016 IEEE International Ultrasonics Symposium (IUS).

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Chen Wang, Wei Shi, Daniel Rueckert, 2016, IEEE Transactions on Medical Imaging.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Frederic Cervenansky, Carole Lartizien, Jan D’hooge, 2019, IEEE Transactions on Medical Imaging.

Detection of Cardiac Events in Echocardiography Using 3D Convolutional Recurrent Neural Networks

Olivier Bernard, Sarah Leclerc, Erik Smistad, 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

Automatic Segmentation and Probe Guidance for Real-Time Assistance of Ultrasound-Guided Femoral Nerve Blocks.

Frank Lindseth, Daniel Høyer Iversen, Erik Smistad, 2017, Ultrasound in medicine & biology.

Highlighting nerves and blood vessels for ultrasound-guided axillary nerve block procedures using neural networks

Ingerid Reinertsen, Daniel Høyer Iversen, Erik Smistad, 2018, Journal of medical imaging.

Real-time gradient vector flow on GPUs using OpenCL

Frank Lindseth, Anne C. Elster, Erik Smistad, 2015, Journal of Real-Time Image Processing.

Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning

Erik Smistad, Andreas Østvik, Torvald Espeland, 2018, DLMIA/ML-CDS@MICCAI.

Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Erik Smistad, Lasse Løvstakken, L. Løvstakken, 2016, LABELS/DLMIA@MICCAI.

Medical image segmentation on GPUs - A comprehensive review

Frank Lindseth, Anne C. Elster, Thomas L. Falch, 2015, Medical Image Anal..

Automatic Real-time Annotation of Important Landmarks in Ultrasound-Guided Femoral Nerve Blocks

Frank Lindseth, Erik Smistad, Linda Leidig, 2015, VCBM.

Segmentation of apical long axis, four- and two-chamber views using deep neural networks

Sarah Leclerc, Erik Smistad, Andreas Østvik, 2019, 2019 IEEE International Ultrasonics Symposium (IUS).

GPU accelerated segmentation and centerline extraction of tubular structures from medical images

Frank Lindseth, Anne C. Elster, Erik Smistad, 2013, International Journal of Computer Assisted Radiology and Surgery.

2D left ventricle segmentation using deep learning

Lasse Lovstakken, Erik Smistad, Andreas Østvik, 2017, 2017 IEEE International Ultrasonics Symposium (IUS).

Real-Time Surface Extraction and Visualization of Medical Images using OpenCL and GPUs

Frank Lindseth, Anne C. Elster, Erik Smistad, 2012 .

Multigrid gradient vector flow computation on the GPU

Frank Lindseth, Erik Smistad, 2014, Journal of Real-Time Image Processing.

Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve

Frank Lindseth, Erik Smistad, F. Lindseth, 2016, IEEE Transactions on Medical Imaging.

Fully Automatic Real-Time Ejection Fraction and MAPSE Measurements in 2D Echocardiography Using Deep Neural Networks

Lasse Lovstakken, Sarah Leclerc, Erik Smistad, 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

Parallelization of Coherent Point Drift for patient registration

Erik Smistad, 2019 .

MIIP: a web-based platform for medical image interpretation training and evaluation focusing on ultrasound

Frank Lindseth, Cecilie Våpenstad, Erik Smistad, 2017, Medical Imaging.

GPU-Based Airway Tree Segmentation and Centerline Extraction

Erik Smistad, 2012 .

High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST

Erik Smistad, Andreas Østvik, André Pedersen, 2019, IEEE Access.

FAST: framework for heterogeneous medical image computing and visualization

Frank Lindseth, Erik Smistad, Mohammadmehdi Bozorgi, 2015, International Journal of Computer Assisted Radiology and Surgery.

FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology pdf

Ingerid Reinertsen, Erik Smistad, Andr'e Pedersen, 2020, IEEE Access.

Deep Learning Applied to Multi-Structure Segmentation in 2D Echocardiography: A Preliminary Investigation of the Required Database Size

Carole Lartizien, Olivier Bernard, Pierre-Marc Jodoin, 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

GPU-Based Airway Segmentation and Centerline Extraction for Image Guided Bronchoscopy

Frank Lindseth, Anne C. Elster, Erik Smistad, 2012 .

A New Tube Detection Filter for Abdominal Aortic Aneurysms

Frank Lindseth, Erik Smistad, Reidar Brekken, 2014, ABDI@MICCAI.

Real-time classification of standard cardiac views in echocardiography using neural networks

Erik Smistad, Andreas Østvik, Svein Arne Aase, 2017, 2017 IEEE International Ultrasonics Symposium (IUS).

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography

Carole Lartizien, Olivier Bernard, Pierre-Marc Jodoin, 2020, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks.

Bjørn Olav Haugen, Lasse Lovstakken, Erik Smistad, 2019, Ultrasound in medicine & biology.

Ultrasound Speckle Reduction Using Generative Adversial Networks

Erik Smistad, Lasse Lovstakken, Andreas Ostvik, 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

Valery Naranjo, Hans Meine, Raúl San José Estépar, 2014, Medical Image Anal..

RU-Net: A refining segmentation network for 2D echocardiography

Carole Lartizien, Olivier Bernard, Pierre-Marc Jodoin, 2019, 2019 IEEE International Ultrasonics Symposium (IUS).

Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity

Frederic Cervenansky, Carole Lartizien, Pierre-Marc Jodoin, 2019 .

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning

Olivier Bernard, Sarah Leclerc, Erik Smistad, 2020, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

FAST: A framework for high-performance medical image computing and visualization

Erik Smistad, 2021, IWOCL.

Myocardial Function Imaging in Echocardiography Using Deep Learning

Lasse Lovstakken, Erik Smistad, Andreas Østvik, 2021, IEEE Transactions on Medical Imaging.

Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology

Ingerid Reinertsen, Erik Smistad, Ilya Belevich, 2021, Frontiers in Medicine.

Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images pdf

Ingerid Reinertsen, Erik Smistad, David Bouget, 2021, ArXiv.

Medical Image Segmentation for Improved Surgical Navigation

Erik Smistad, 2015 .

Fast Surface Extraction and Visualization of Medical Images using OpenCL and GPUs

Frank Lindseth, Anne C. Elster, Erik Smistad, 2011 .

Airway Segmentation and Centerline Extraction from Thoracic CT – Comparison of a New Method to State of the Art Commercialized Methods

Pall Jens Reynisson, Erlend Fagertun Hofstad, Håkon Olav Leira, 2015, PloS one.