Blar i SINTEF Open på forfatter "Smistad, Erik"

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    • 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 (Journal article; Peer reviewed, 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 ...

    • 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 measurements ...

    • 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 ...

    • 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 (Peer reviewed; Journal article, 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 ...

    • 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 (Peer reviewed; Journal article, 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 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 (Peer reviewed; Journal article, 2022)
      Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the ...

    • 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, ...

    • 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 ...