Skip to main navigation Skip to search Skip to main content

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

  • Vanda Czipczer
  • , Bernadett Kolozsvári
  • , Borbála Deák-Karancsi
  • , Marta E. Capala
  • , Rachel A. Pearson
  • , Emőke Borzási
  • , Zsófia Együd
  • , Szilvia Gaál
  • , Gyöngyi Kelemen
  • , Renáta Kószó
  • , Viktor Paczona
  • , Zoltán Végváry
  • , Zsófia Karancsi
  • , Ádám Kékesi
  • , Edina Czunyi
  • , Blanka H. Irmai
  • , Nóra G. Keresnyei
  • , Petra Nagypál
  • , Renáta Czabány
  • , Bence Gyalai
    • Bulcsú P. Tass, Balázs Cziria, Cristina Cozzini, Lloyd Estkowsky, Lehel Ferenczi, András Frontó, Ross Maxwell, István Megyeri, Michael Mian, Tao Tan, Jonathan Wyatt, Florian Wiesinger, Katalin Hideghéty, Hazel McCallum, Steven F. Petit, László Ruskó
  • GE Healthcare
  • Erasmus University Rotterdam
  • Newcastle University
  • University of Szeged

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Fingerprint

Dive into the research topics of 'Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning'. Together they form a unique fingerprint.
Sort by

Medicine and Dentistry