Blar i NTNU Open på forfatter "Huynh, Bao Ngoc"

Viser treff 1-4 av 4

    • A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers 

      Grøndahl, Aurora Rosvoll; Knudtsen, Ingerid Skjei; Huynh, Bao Ngoc; Mulstad, Martine; Moe, Yngve Mardal; Knuth, Franziska; Tomic, Oliver; Indahl, Ulf Geir; Torheim, Turid Katrine Gjerstad; Dale, Einar; Malinen, Eirik; Futsæther, Cecilia Marie (Peer reviewed; Journal article, 2021)
      Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) ...

    • Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics 

      Huynh, Bao Ngoc; Grøndahl, Aurora Rosvoll; Tomic, Oliver; Liland, Kristian Hovde; Knudtsen, Ingerid Søberg Skjei; Hoebers, Frank; van Elmpt, Wouter; Malinen, Eirik; Dale, Einar; Futsæther, Cecilia Marie (Peer reviewed; Journal article, 2023)
      Background: Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which ...

    • MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts 

      Knuth, Franziska Hanna; Adde, Ingvild Askim; Huynh, Bao Ngoc; Grøndahl, Aurora Rosvoll; Winter, René; Negård, Anne; Holmedal, Stein Harald; Meltzer, Sebastian; Ree, Anne Hansen; Flatmark, Kjersti; Dueland, Svein; Hole, Knut Håkon; Seierstad, Therese; Redalen, Kathrine; Futsæther, Cecilia Marie (Peer reviewed; Journal article, 2021)
      Background Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties ...

    • MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts 

      Knuth, Franziska Hanna; Adde, Ingvild Askim; Huynh, Bao Ngoc; Groendahl, Aurora Rosvoll; Winter, René Mario; Negård, Anne; Holmedal, Stein Harald; Meltzer, Sebastian; Ree, Anne Hansen; Flatmark, Kjersti; Dueland, Svein; Hole, Knut Håkon; Seierstad, Therese; Redalen, Kathrine Røe; Futsaether, Cecilie Maria (Peer reviewed; Journal article, 2022)
      Background: Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties ...