Browsing NTNU Open by Author "Smistad, Erik"

Now showing items 1-20 of 31

    • 3D Segmentation of the Left Ventricle in Echocardiography Based on Deep Learning 

      Steinsland, Erik Nikolai (Master thesis, 2020)
      3D ekkokardiografi har blitt et nyttig verktøy for nøyaktig segmentering og volummåling av venstre ventrikkel, da ultralyd anses som trygt og tilgjengelig sammenlignet med andre medisinske avbildningsmetoder. Manuell sporing ...

    • A new GPU-based Hybrid Approach to 3D Ultrasound Reconstruction - Quality Reconstruction with Top-End Performance in FAST 

      Fagerli, Ruben Håskjold (Master thesis, 2016)
      Ultrasound imaging is a versatile, portable, and low cost medical imaging modality. It produces real-time data from a local area in the scanned person, or object, useful in use cases such as intra-operative imaging. Freehand ...

    • 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 ...

    • Airway segmentation and centerline extraction from thoracic CT - Comparison of a new method to state of the art commercialized methods 

      Reynisson, Pall Jens; Scali, Marta; Smistad, Erik; Hofstad, Erlend Fagertun; Leira, Håkon Olav; Lindseth, Frank; Hernes, Toril A. Nagelhus; Amundsen, Tore; Sorger, Hanne; Langø, Thomas (Peer reviewed; Journal article, 2015)
      Introduction Our motivation is increased bronchoscopic diagnostic yield and optimized preparation, for navigated bronchoscopy. In navigated bronchoscopy, virtual 3D airway visualization is often used to guide a bronchoscopic ...

    • 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 ...

    • Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning 

      Hu, Jieyu; Olaisen, Sindre Hellum; Smistad, Erik; Dalen, Håvard; Løvstakken, Lasse (Journal article; Peer reviewed, 2023)
      Objective: Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume ...

    • 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 ...

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

      Pettersen, Henrik P Sahlin; Belevich, Ilya; Røyset, Elin Synnøve; Smistad, Erik; Simpson, Melanie Rae; Jokitalo, Eija; Reinertsen, Ingerid; Bakke, Ingunn; Pedersen, André (Peer reviewed; Journal article, 2022)
      Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase ...

    • 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 ...

    • Deep Learning in the Echocardiography Workflow: Challenges and Opportunities 

      Pasdeloup, David (Doctoral theses at NTNU;2023:390, Doctoral thesis, 2023)
      Echocardiography is the most widely used imaging technique for cardiac imaging due to its availability, low-cost and real-time capabilities. With numerous indicators of heart function, it now enables precise diagnosis of ...

    • FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology 

      Pedersen, André; Valla, Marit; Bofin, Anna Mary; Perez de Frutos, Javier; Reinertsen, Ingerid; Smistad, Erik (Peer reviewed; Journal article, 2021)
      Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, ...

    • 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 ...

    • GPU-Based Airway Tree Segmentation and Centerline Extraction 

      Smistad, Erik (Master thesis, 2012)
      Lung cancer is one of the deadliest and most common types of cancer inNorway. Early and precise diagnosis is crucial for improving the survivalrate. Diagnosis is often done by extracting a tissue sample in the lung throughthe ...

    • H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images 

      Pedersen, André; Smistad, Erik; Rise, Tor Vikan; Dale, Vibeke Grotnes; Pettersen, Henrik P Sahlin; Nordmo, Tor-Arne Schmidt; Bouget, David Nicolas Jean-Mar; Reinertsen, Ingerid; Valla, Marit (Journal article; Peer reviewed, 2022)
      Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation ...

    • 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 ...

    • Medical Image Segmentation for Improved Surgical Navigation 

      Smistad, Erik (Doctoral thesis at NTNU;2015:236, Doctoral thesis, 2015)
      During this project, developing image segmentation software for different types of processors was found to be challenging due to several factors, such as driver errors, processor differences, and the need for low level ...

    • 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 ...

    • Patient Adaptive Imaging in Echocardiography 

      Gundersen, Erlend Løland (Master thesis, 2022)
      Denne oppgaven foreslår en dyp læring-basert metode for generalisering av B-modus-signalbehandlingen til Vivid E95 ultralydskanneren fra GE, med det formål å forbedre bildekvaliteten i ekkokardiografi. En annen metode ble ...