Blar i NTNU Open på forfatter "Smistad, Erik"
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Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning
Smistad, Erik; Salte, Ivar Mjåland; Østvik, Andreas; Melichova, Daniela; Nguyen, Thuy Mi; Haugaa, Kristina; Brunvand, Harald; Edvardsen, Thor; Leclerc, Sarah; Bernard, Olivier; Grenne, Bjørnar; Løvstakken, Lasse (Peer reviewed; Journal article, 2020)Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ... -
Real-Time Echocardiography Guidance for Optimized Apical Standard Views
Pasdeloup, David Francis Pierre; Olaisen, Sindre Hellum; Østvik, Andreas; Sæbø, Sigbjørn; Pettersen, Håkon Neergaard; Holte, Espen; Grenne, Bjørnar; Stølen, Stian Bergseng; Smistad, Erik; Aase, Svein Arne; Dalen, Håvard; Løvstakken, Lasse (Journal article; Peer reviewed, 2022) -
Real-time segmentation of blood vessels, nerves and bone in ultrasound-guided regional anesthesia using deep learning
Smistad, Erik; Lie, Torgrim; Johansen, Kaj Fredrik (Peer reviewed; Journal article, 2021)Images from ultrasound-guided regional anesthesia procedures can be difficult to interpret, especially by non-experts. In this work, deep convolutional neural networks were used to segment blood vessels, nerves and bone ... -
Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks
Østvik, Andreas; Smistad, Erik; Aase, Svein Arne; Haugen, Bjørn Olav; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, ... -
Real-time temporal coherent left ventricle segmentation using convolutional LSTMs
Smistad, Erik; Salte, Ivar Mjåland; Dalen, Håvard; Løvstakken, Lasse (Peer reviewed; Journal article, 2021) -
Segmentation of Medical Image Data using Level Set Methods
Hunderi, Andreas Helmich; Karunakaran, Neshahavan (Master thesis, 2013)The field of medical image analysis is becoming an increasingly important part of the medical profession. Advancements in the field of medical imaging techniques results in images and volumes with an increasing level of ... -
Segmentation of parasternal long axis views using deep learning
Smistad, Erik; Dalen, Håvard; Grenne, Bjørnar; Løvstakken, Lasse (Journal article; Peer reviewed, 2022) -
Tracking-based mitral annular plane systolic excursion (MAPSE) measurement using deep learning in B-mode ultrasound
Smistad, Erik; Østvik, Andreas; Grue, Jahn Frederik; Dalen, Håvard; Løvstakken, Lasse (Peer reviewed; Journal article, 2022)Mitral annular plane systolic excursion (MAPSE) is an important measure of left ventricular function. Current clinical practice is to measure it manually using M-mode ultrasound imaging which has several disadvantages such ... -
Ultrasound speckle reduction using generative adversial networks
Dietrichson, Fabian Sødal; Smistad, Erik; Østvik, Andreas; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)Generative adversial networks (GANs) have shown its ability to create realistic and accurate image-to-image transformation. The goal of this work was to investigate whether deep convolutional GANs can learn to perform ... -
Vessel detection in ultrasound images using deep convolutional neural networks
Smistad, Erik; Løvstakken, Lasse (Journal article; Peer reviewed, 2016)Deep convolutional neural networks have achieved great results on image classification problems. In this paper, a new method using a deep convolutional neural network for detecting blood vessels in B-mode ultrasound images ...