Browsing NTNU Open by Author "Østvik, Andreas"
Now showing items 1-20 of 23
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Age Estimation from B-mode Echocardiography with 3D Convolutional Neural Networks
Fiorito, Adrian Meidell (Master thesis, 2019)Ekkokardiografi er en ikke-invasiv og sikker metode som bruker ultralyd for avbildning av hjertet. Som på mange andre felt utfører metoder for dyp læring, spesifikt \textit{Convolutional neural networks} (CNNs), oppgaver ... -
Annotation Web - An open-source web-based annotation tool for ultrasound images
Smistad, Erik; Østvik, Andreas; Løvstakken, Lasse (Peer reviewed; Journal article, 2021)The use of deep learning and other machine learning techniques requires large amounts of annotated image data. There exist several tools to annotate images, however to our knowledge there are no tools made specifically for ... -
Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography
Salte, Ivar M.; Østvik, Andreas; Smistad, Erik; Melichova, Daniela; Nguyen, Thuy Mi; Karlsen, Sigve; Brunvand, Harald; Haugaa, Kristina H.; Edvardsen, Thor; Løvstakken, Lasse; Grenne, Bjørnar (Journal article; Peer reviewed, 2021)Objectives This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible ... -
Automatic analysis in echocardiography using machine learning
Østvik, Andreas (Doctoral theses at NTNU, 2021:230, Doctoral thesis, 2021)Echocardiography is the cornerstone of modern cardiac imaging due to its availability, low cost and real-time functionality. The modality has enabled sophisticated non-invasive evaluation of the hearts morphophysiology, ... -
Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases
Olaisen, Sindre Hellum; Smistad, Erik; Espeland, Torvald; Hu, Jieyu; Pasdeloup, David Francis Pierre; Østvik, Andreas; Aakhus, Svend; Rösner, Assami; Malm, Siri; Stylidis, Michael; Holte, Espen; Grenne, Bjørnar Leangen; Løvstakken, Lasse; Dalen, Håvard (Peer reviewed; Journal article, 2023)Aims Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully ... -
Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning
Østvik, Andreas; Smistad, Erik; Espeland, Torvald; Berg, Erik Andreas Rye; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)Recent studies in the field of deep learning suggest that motion estimation can be treated as a learnable problem. In this paper we propose a pipeline for functional imaging in echocardiography consisting of four central ... -
Can a Dinosaur Think? Implementation of Artificial Intelligence in Extracorporeal Shock Wave Lithotripsy
Muller, Sebastien; Abildsnes, Håkon; Østvik, Andreas; Kragset, Oda; Gangås, Inger Sofie Hovd; Birke, Harriet; Langø, Thomas; Arum, Carl-Jørgen (Peer reviewed; Journal article, 2021)Background: Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments. Objective: This proof-of-concept project was initiated to develop artificial ... -
Deep Learning based Classification of Cardiac Events in Echocardiography
Langerud, Maren Andrea (Master thesis, 2022)Abstract will be available on 2023-01-29 -
Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study
Salte, Ivar Mjåland; Østvik, Andreas; Olaisen, Sindre Hellum; Karlsen, Sigve; Dahlslett, Thomas; Smistad, Erik; Eriksen-Volnes, Torfinn Kirknes; Brunvand, Harald; Haugaa, Kristina Ingrid Helena Hermann; Edvardsen, Thor; Dalen, Håvard; Løvstakken, Lasse; Grenne, Bjørnar Leangen (Peer reviewed; Journal article, 2023)Aims Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated ... -
Echocardiographic Reference Ranges of Global Longitudinal Strain for All Cardiac Chambers Using Guideline-Directed Dedicated Views
Nyberg, John; Jakobsen, Even Olav; Østvik, Andreas; Holte, Espen; Stølen, Stian; Løvstakken, Lasse; Grenne, Bjørnar Leangen; Dalen, Håvard (Peer reviewed; Journal article, 2023)Background Myocardial deformation by echocardiographic strain imaging is a key measurement in cardiology, providing valuable diagnostic and prognostic information. Reference ranges for strain should be established from ... -
Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks
Smistad, Erik; Østvik, Andreas; Salte, Ivar Mjåland; Leclerc, Sarah; Bernard, Olivier; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)Cardiac ultrasound measurements such as left ventricular volume, ejection fraction (EF) and mitral annular plane systolic excursion (MAPSE) are time consuming and highly observer dependent. In this work, we investigate if ... -
High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST
Smistad, Erik; Østvik, Andreas; Pedersen, Andrè (Journal article; Peer reviewed, 2019)Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference ... -
Myocardial Function Imaging in Echocardiography Using Deep Learning
Østvik, Andreas; Salte, Ivar Mjåland; Smistad, Erik; Nguyen, Thuy Mi; Melichova, Daniela; Brunvand, Harald; Haugaa, Kristina; Edvardsen, Thor; Grenne, Bjørnar; Løvstakken, Lasse (Journal article; Peer reviewed, 2021)Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated ... -
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 classification of mediastinal lymph nodes in endobronchial ultrasound images using deep neural networks
Rødde, Mia (Master thesis, 2024)Bakgrunn og formål Korrekt klassifisering av lymfeknute stasjoner i mediastinum er nødvendig for å bestemme riktig lungekreft stadium. Dette er informasjon som brukes til å gi pasienten den mest effektive behandlingen. Det ... -
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 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 adaptation of dynamic movement primitives for moving targets
Østvik, Andreas; Grøtli, Esten Ingar; Vagia, Marialena; Gravdahl, Jan Tommy (Chapter, 2021) -
Reinforcement Learning for Robotic Ultrasound
Ackre, Susanne Dorethea (Master thesis, 2022)Denne masteroppgaven er et studie av “deep reinforcement learning” for robotisert ultralyd. Å bruke autonome roboter i helsesektoren er et komplekst bruksområde med mange faktorer å ta hensyn til. For eksempel, utfordingen ... -
Reinforcement learning for robotic soft-body interaction
Jakobsen, Herman Kolstad (Master thesis, 2021)Innen klinisk medisin ansees det som utfordrende å benytte autonome robotsystemer. Håndtering av bevegelige objekter og myke materialer, samt variasjon mellom pasienter, har vist seg å være en hindring for robotassisterte ...