Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at, first published .
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study


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  94. Tada D, Teng P, Vyapari K, Banola A, Foster G, Diaz E, Kim G, Goldin J, Abtin F, McNitt-Gray M, Brown M. Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning. Journal of Medical Imaging 2024;11(03) View
  95. Takeya A, Watanabe K, Haga A. Fine structural human phantom in dentistry and instance tooth segmentation. Scientific Reports 2024;14(1) View
  96. Zeverino M, Piccolo C, Marguet M, Jeanneret-Sozzi W, Bourhis J, Bochud F, Moeckli R. Sensitivity of automated and manual treatment planning approaches to contouring variation in early-breast cancer treatment. Physica Medica 2024;123:103402 View
  97. Sahlsten J, Jaskari J, Wahid K, Ahmed S, Glerean E, He R, Kann B, Mäkitie A, Fuller C, Naser M, Kaski K. Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning. Communications Medicine 2024;4(1) View
  98. Dot G, Chaurasia A, Dubois G, Savoldelli C, Haghighat S, Azimian S, Taramsari A, Sivaramakrishnan G, Issa J, Dubey A, Schouman T, Gajny L. DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation. Journal of Dentistry 2024;147:105130 View
  99. Wahid K, Sahin O, Kundu S, Lin D, Alanis A, Tehami S, Kamel S, Duke S, Sherer M, Rasmussen M, Korreman S, Fuentes D, Cislo M, Nelms B, Christodouleas J, Murphy J, Mohamed A, He R, Naser M, Gillespie E, Fuller C. Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation. JCO Clinical Cancer Informatics 2024;(8) View
  100. Bakx N, Van der Sangen M, Theuws J, Bluemink J, Hurkmans C. Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy. Acta Oncologica 2024;63:477 View
  101. Akramova R, Watanabe Y. Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation. Journal of Applied Clinical Medical Physics 2024 View
  102. Podobnik G, Ibragimov B, Tappeiner E, Lee C, Sung Kim J, Mesbah Z, Modzelweski R, Ma Y, Yang F, Rudecki M, Wodziński M, Peterlin P, Strojan P, Vrtovec T. HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Radiotherapy and Oncology 2024:110410 View
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Books/Policy Documents

  1. Naser M, Wahid K, van Dijk L, He R, Abdelaal M, Dede C, Mohamed A, Fuller C. Head and Neck Tumor Segmentation and Outcome Prediction. View
  2. Zheng H, Nan L, Yang Q, Yang M, Yang T, Suandi T. The 2021 International Conference on Smart Technologies and Systems for Internet of Things. View
  3. Mody P, Chaves-de-Plaza N, Hildebrandt K, Staring M. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. View
  4. Sellner J, Seidlitz S, Studier-Fischer A, Motta A, Özdemir B, Müller-Stich B, Nickel F, Maier-Hein L. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. View
  5. Podobnik G, Strojan P, Peterlin P, Ibragimov B, Vrtovec T. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. View
  6. Boon I, Yap M, Au Yong T, Boon C. Machine Learning and Artificial Intelligence in Radiation Oncology. View
  7. Khriguian J, Gharzai L, Heukelom J, McDonald B, Fuller C. A Practical Guide to MR-Linac. View