Blar i Institutt for fysikk på forfatter "Adde, Ingvild Askim"

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    • Deep Learning For Automatic Segmentation Of Rectal Cancer On Magnetic Resonance Images From Two Independent Cohorts 

      Adde, Ingvild Askim (Master thesis, 2021)
      Heniskt: Inntegning av kreftsvulstvolumet er en viktig del av både kvantitativ bildeanalyse og strålebehandling. Dette er en tidkrevende oppgave som er forbundet med usikkerhet grunnet interobservatørvariabilitet. Det ville ...

    • 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 ...