Browsing NTNU Open by Author "Pedersen, André"

Now showing items 1-15 of 15

    • 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 ...

    • Efficient multilabel tissue segmentation of histopathological images using hybrid vision transformer 

      Drange, Markus Mangersnes (Master thesis, 2023)
      Histopatologi er et sentral felt innen medisin, og gjør det mulig for klinikere å gi nøyaktige diagnoser på cellenivå for sykdommer som spenner fra kreft til infeksjoner. Studiet av mikroskopiske bilder er imidlertid ...

    • 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, ...

    • Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task 

      Bouget, David Nicolas Jean-Marie; Eijgelaar, Roelant; Pedersen, André; Kommers, Ivar; Ardon, Hilko; Barkhof, Frederik; Bello, Lorenzo; Berger, Mitchel S.; Nibali, Marco Conti; Furtner, Julia; Fyllingen, Even Hovig; Hervey-Jumper, Shawn; Idema, Albert J. S.; Kiesel, Barbara; Kloet, Alfred; Mandonnet, Emmanuel; Müller, Domenique M. J.; Robe, Pierre; Rossi, Marco; Sagberg, Lisa Millgård; Sciortino, Tommaso; van den Brink, Wimar A.; Wagemakers, Michiel; Widhalm, Georg; Witte, Marnix G.; Zwinderman, Aeilko H.; Reinertsen, Ingerid; Hamer, Philip C De Witt; Solheim, Ole (Journal article; Peer reviewed, 2021)
      For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude ...

    • Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations 

      Kommers, Ivar; Bouget, David Nicolas Jean-Marie; Pedersen, André; Eijgelaar, Roelant; Ardon, Hilko; Barkhof, Frederik; Bello, Lorenzo; Berger, Mitchel S.; Nibali, Marco Conti; Furtner, Julia; Fyllingen, Even Hovig; Hervey-Jumper, Shawn; Idema, Albert J. S.; Kiesel, Barbara; Kloet, Alfred; Mandonnet, Emmanuel; Müller, Domenique M. J.; Robe, Pierre; Rossi, Marco; Sagberg, Lisa Millgård; Sciortino, Tommaso; van den Brink, Wimar A.; Wagemakers, Michiel; Widhalm, Georg; Witte, Marnix G.; Zwinderman, Aeilko H.; Reinertsen, Ingerid; Solheim, Ole; De Witt Hamer, Philip C. (Peer reviewed; Journal article, 2021)
      Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated ...

    • 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 ...

    • Lungs and Lobes Semantic Segmentation in Mediastinal CT Scans Using 3D Convolutional Neural Networks 

      Kristin Schive Hjelde (Master thesis, 2020)
      Pulmonal CT-bildeanalyse er en viktig del av vurderingen og behandlingsplanleggingen av forskjellige lungesykdommer. Metoden krever ofte at lungene skilles fra de omkringliggende strukturene, en prosess kjent som ...

    • Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding 

      Bouget, David Nicolas Jean-Mar; Pedersen, André; Vanel, Johanna; Leira, Håkon Olav; Langø, Thomas (Peer reviewed; Journal article, 2022)
      As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation ...

    • Meningioma Segmentation in T1-Weighted MRI Leveraging Global Context and Attention Mechanisms 

      Bouget, David Nicolas Jean-Marie; Pedersen, André; Hosainey, Sayied Abdol Mohieb; Solheim, Ole; Reinertsen, Ingerid (Peer reviewed; Journal article, 2021)
      Purpose: Meningiomas are the most common type of primary brain tumor, accounting for ~30% of all brain tumors. A substantial number of these tumors are never surgically removed but rather monitored over time. Automatic and ...

    • Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events 

      Yan, Melissa; Høvik, Lise Husby; Pedersen, André; Gustad, Lise Tuset; Nytrø, Øystein (Chapter, 2021)
      Adverse event (AE) reports contain notes detailing procedural and guideline deviations, and unwanted incidents that can bring harm to patients. Available datasets mainly focus on vigilance or post-market surveillance of ...

    • Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting 

      Bouget, David Nicolas Jean-Mar; Pedersen, André; Jakola, Asgeir S.; Kavouridis, Vasileios; Emblem, Kyrre Eeg; Eijgelaar, Roelant S; Kommers, Ivar; Ardon, Hilko; Barkhof, Frederik; Bello, Lorenzo; Berger, Mitchel S.; Nibali, Marco Conti; Furtner, Julia; Hervey-Jumper, Shawn; Idema, Albert J. S.; Kiesel, Barbara; Kloet, Alfred; Mandonnet, Emmanuel; Müller, Domenique M. J.; Robe, Pierre A; Rossi, Marco; Sciortino, Tommaso; van den Brink, Wimar A.; Wagemakers, Michiel; Widhalm, Georg; Witte, Marnix G.; Zwinderman, Aeilko H.; Hamer, Philip C De Witt; Solheim, Ole; Reinertsen, Ingerid (Peer reviewed; Journal article, 2022)
      For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods ...

    • Pulmonary Tumor Segmentation Utilizing Mixed-Supervision in a Teacher-Student Framework 

      Fredriksen, Vemund; Sevle, Svein Ole Matheson (Master thesis, 2021)
      Kreft er en av de fremste dødsårsakene i den utviklede verden, og den mest dødelige typen er lungekreft. Kliniske eksperter er avhengig av avanserte medisinske bildebehandlingsteknikker og visuell analyse for å oppdage ...

    • Pulmonary Tumor Segmentation Utilizing Mixed-Supervision in a Teacher-Student Framework 

      Fredriksen, Vemund; Sevle, Svein Ole Matheson (Master thesis, 2021)
      Kreft er en av de fremste dødsårsakene i den utviklede verden, og den mest dødelige typen er lungekreft. Kliniske eksperter er avhengig av avanserte medisinske bildebehandlingsteknikker og visuell analyse for å oppdage ...

    • Sonopermeation Enhances Uptake and Therapeutic Effect of Free and Encapsulated Cabazitaxel 

      Snipstad, Sofie; Mørch, Ýrr; Sulheim, Einar; Åslund, Andreas; Pedersen, André; Hansen, Rune; Davies, Catharina de Lange; Berg, Sigrid (Peer reviewed; Journal article, 2021)
      Delivery of drugs and nanomedicines to tumors is often heterogeneous and insufficient and, thus, of limited efficacy. Microbubbles in combination with ultrasound have been found to improve delivery to tumors, enhancing ...

    • Teacher-student approach for lung tumor segmentation from mixed-supervised datasets 

      Fredriksen, Vemund; Sevle, Svein Ole M.; Pedersen, André; Langø, Thomas; Kiss, Gabriel; Lindseth, Frank (Journal article; Peer reviewed, 2022)
      Purpose Cancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating ...