This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Machine Learning (1465) Clinical Informatics (1022) Imaging Informatics (193) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Innovations and Technology in Cancer Care (423)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26151, 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

Authors of this article:

Stanislav Nikolov1 Author Orcid Image ;   Sam Blackwell1 Author Orcid Image ;   Alexei Zverovitch2 Author Orcid Image ;   Ruheena Mendes3 Author Orcid Image ;   Michelle Livne2 Author Orcid Image ;   Jeffrey De Fauw1 Author Orcid Image ;   Yojan Patel2 Author Orcid Image ;   Clemens Meyer1 Author Orcid Image ;   Harry Askham1 Author Orcid Image ;   Bernadino Romera-Paredes1 Author Orcid Image ;   Christopher Kelly2 Author Orcid Image ;   Alan Karthikesalingam2 Author Orcid Image ;   Carlton Chu1 Author Orcid Image ;   Dawn Carnell3 Author Orcid Image ;   Cheng Boon4 Author Orcid Image ;   Derek D'Souza3 Author Orcid Image ;   Syed Ali Moinuddin3 Author Orcid Image ;   Bethany Garie1 Author Orcid Image ;   Yasmin McQuinlan1 Author Orcid Image ;   Sarah Ireland1 Author Orcid Image ;   Kiarna Hampton1 Author Orcid Image ;   Krystle Fuller1 Author Orcid Image ;   Hugh Montgomery5 Author Orcid Image ;   Geraint Rees5 Author Orcid Image ;   Mustafa Suleyman6 Author Orcid Image ;   Trevor Back1 Author Orcid Image ;   Cían Owen Hughes2 Author Orcid Image ;   Joseph R Ledsam7 Author Orcid Image ;   Olaf Ronneberger1 Author Orcid Image
Article Authors Cited by (145) Tweetations (12) Metrics

Journals

  1. Ali S, Jha D, Ghatwary N, Realdon S, Cannizzaro R, Salem O, Lamarque D, Daul C, Riegler M, Anonsen K, Petlund A, Halvorsen P, Rittscher J, de Lange T, East J. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data 2023;10(1) View
  2. Tryggestad E, Anand A, Beltran C, Brooks J, Cimmiyotti J, Grimaldi N, Hodge T, Hunzeker A, Lucido J, Laack N, Momoh R, Moseley D, Patel S, Ridgway A, Seetamsetty S, Shiraishi S, Undahl L, Foote R. Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer. Frontiers in Oncology 2022;12 View
  3. Krishnamurthy R, Mummudi N, Goda J, Chopra S, Heijmen B, Swamidas J. Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy. JCO Global Oncology 2022;(8) View
  4. Dot G, Schouman T, Dubois G, Rouch P, Gajny L. Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework. European Radiology 2022;32(6):3639 View
  5. Mojtahedi M, Kappelhof M, Ponomareva E, Tolhuisen M, Jansen I, Bruggeman A, Dutra B, Yo L, LeCouffe N, Hoving J, van Voorst H, Brouwer J, Terreros N, Konduri P, Meijer F, Appelman A, Treurniet K, Coutinho J, Roos Y, van Zwam W, Dippel D, Gavves E, Emmer B, Majoie C, Marquering H. Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke. Diagnostics 2022;12(3):698 View
  6. Rhee D, Akinfenwa C, Rigaud B, Jhingran A, Cardenas C, Zhang L, Prajapati S, Kry S, Brock K, Beadle B, Shaw W, O'Reilly F, Parkes J, Burger H, Fakie N, Trauernicht C, Simonds H, Court L. Automatic contouring QA method using a deep learning–based autocontouring system. Journal of Applied Clinical Medical Physics 2022;23(8) View
  7. Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clinical Oncology 2023;35(6):354 View
  8. Francis S, Pooloth G, Singam S, Puzhakkal N, Pulinthanathu Narayanan P, Pottekkattuvalappil Balakrishnan J. SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images. International Journal of Imaging Systems and Technology 2023;33(1):175 View
  9. Zhovannik I, Bontempi D, Romita A, Pfaehler E, Primakov S, Dekker A, Bussink J, Traverso A, Monshouwer R. Segmentation Uncertainty Estimation as a Sanity Check for Image Biomarker Studies. Cancers 2022;14(5):1288 View
  10. Elisabeth Olsson C, Suresh R, Niemelä J, Akram S, Valdman A. Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy. Physics and Imaging in Radiation Oncology 2022;22:67 View
  11. Shanker R, Zhang M, Ginat D. Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks. Diagnostics 2022;12(7):1553 View
  12. Choi B, Olberg S, Park J, Kim J, Shrestha D, Yaddanapudi S, Furutani K, Beltran C. Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation. Medical Physics 2023;50(8):5075 View
  13. Weissmann T, Huang Y, Fischer S, Roesch J, Mansoorian S, Ayala Gaona H, Gostian A, Hecht M, Lettmaier S, Deloch L, Frey B, Gaipl U, Distel L, Maier A, Iro H, Semrau S, Bert C, Fietkau R, Putz F. Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy. Frontiers in Oncology 2023;13 View
  14. Huiskes M, Astreinidou E, Kong W, Breedveld S, Heijmen B, Rasch C. Dosimetric impact of adaptive proton therapy in head and neck cancer – A review. Clinical and Translational Radiation Oncology 2023;39:100598 View
  15. Jin D, Guo D, Ge J, Ye X, Lu L. Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era. Journal of the National Cancer Center 2022;2(4):306 View
  16. Walker Z, Bartley G, Hague C, Kelly D, Navarro C, Rogers J, South C, Temple S, Whitehurst P, Chuter R. Evaluating the Effectiveness of Deep Learning Contouring across Multiple Radiotherapy Centres. Physics and Imaging in Radiation Oncology 2022;24:121 View
  17. D’Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D, Piras A, Di Dio C, Iezzi M, Preziosi F, Menna S, Quaranta F, Boschetti A, Marras M, Miccichè F, Gallus R, Indovina L, Bussu F, Valentini V, Cusumano D, Mattiucci G. Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center. International Journal of Environmental Research and Public Health 2022;19(15):9057 View
  18. Jiang J, Elguindi S, Berry S, Onochie I, Cervino L, Deasy J, Veeraraghavan H. Nested block self‐attention multiple resolution residual network for multiorgan segmentation from CT. Medical Physics 2022;49(8):5244 View
  19. Ng C, Leung V, Hung R. Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy. Applied Sciences 2022;12(22):11681 View
  20. Chufal K, Ahmad I, Chowdhary R. Artificial intelligence in radiation oncology: How far have we reached?. International Journal of Molecular and Immuno Oncology 2023;8:9 View
  21. Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman B, Litjens G, Menze B, Ronneberger O, Summers R, van Ginneken B, Bilello M, Bilic P, Christ P, Do R, Gollub M, Heckers S, Huisman H, Jarnagin W, McHugo M, Napel S, Pernicka J, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin J, Ourselin S, Wiesenfarth M, Arbeláez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson A, Maier-Hein L, Cardoso M. The Medical Segmentation Decathlon. Nature Communications 2022;13(1) View
  22. Claessens M, Vanreusel V, De Kerf G, Mollaert I, Löfman F, Gooding M, Brouwer C, Dirix P, Verellen D. Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm. Physics in Medicine & Biology 2022;67(11):115014 View
  23. Garrett Fernandes M, Bussink J, Stam B, Wijsman R, Schinagl D, Monshouwer R, Teuwen J. Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing. Radiotherapy and Oncology 2021;165:52 View
  24. Peng Y, Liu Y, Shen G, Chen Z, Chen M, Miao J, Zhao C, Deng J, Qi Z, Deng X. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning. Oral Oncology 2023;136:106261 View
  25. Kihara S, Koike Y, Takegawa H, Anetai Y, Nakamura S, Tanigawa N, Koizumi M. Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment. Medical Dosimetry 2023;48(1):20 View
  26. Shi F, Hu W, Wu J, Han M, Wang J, Zhang W, Zhou Q, Zhou J, Wei Y, Shao Y, Chen Y, Yu Y, Cao X, Zhan Y, Zhou X, Gao Y, Shen D. Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nature Communications 2022;13(1) View
  27. Zhong Z, He L, Chen C, Yang X, Lin L, Yan Z, Tian M, Sun Y, Zhan Y. Full‐scale attention network for automated organ segmentation on head and neck CT and MR images. IET Image Processing 2023;17(3):660 View
  28. Santoro M, Strolin S, Paolani G, Della Gala G, Bartoloni A, Giacometti C, Ammendolia I, Morganti A, Strigari L. Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. Applied Sciences 2022;12(7):3223 View
  29. Tappeiner E, Welk M, Schubert R. Tackling the class imbalance problem of deep learning-based head and neck organ segmentation. International Journal of Computer Assisted Radiology and Surgery 2022;17(11):2103 View
  30. Arrarte Terreros N, van Willigen B, Niekolaas W, Tolhuisen M, Brouwer J, Coutinho J, Beenen L, Majoie C, van Bavel E, Marquering H. Occult blood flow patterns distal to an occluded artery in acute ischemic stroke. Journal of Cerebral Blood Flow & Metabolism 2022;42(2):292 View
  31. Bai T, Balagopal A, Dohopolski M, Morgan H, McBeth R, Tan J, Lin M, Sher D, Nguyen D, Jiang S. A Proof-of-Concept Study of Artificial Intelligence–assisted Contour Editing. Radiology: Artificial Intelligence 2022;4(5) View
  32. Naser M, Wahid K, Ahmed S, Salama V, Dede C, Edwards B, Lin R, McDonald B, Salzillo T, He R, Ding Y, Abdelaal M, Thill D, O'Connell N, Willcut V, Christodouleas J, Lai S, Fuller C, Mohamed A. Quality assurance assessment of intra‐acquisition diffusion‐weighted and T2‐weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy. Medical Physics 2023;50(4):2089 View
  33. Primakov S, Ibrahim A, van Timmeren J, Wu G, Keek S, Beuque M, Granzier R, Lavrova E, Scrivener M, Sanduleanu S, Kayan E, Halilaj I, Lenaers A, Wu J, Monshouwer R, Geets X, Gietema H, Hendriks L, Morin O, Jochems A, Woodruff H, Lambin P. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 2022;13(1) View
  34. Schellenberg M, Dreher K, Holzwarth N, Isensee F, Reinke A, Schreck N, Seitel A, Tizabi M, Maier-Hein L, Gröhl J. Semantic segmentation of multispectral photoacoustic images using deep learning. Photoacoustics 2022;26:100341 View
  35. Zhao Q, Wang G, Lei W, Fu H, Qu Y, Lu J, Zhang S, Zhang S. Segmentation of multiple Organs‐at‐Risk associated with brain tumors based on coarse‐to‐fine stratified networks. Medical Physics 2023;50(7):4430 View
  36. Ye X, Guo D, Ge J, Yan S, Xin Y, Song Y, Yan Y, Huang B, Hung T, Zhu Z, Peng L, Ren Y, Liu R, Zhang G, Mao M, Chen X, Lu Z, Li W, Chen Y, Huang L, Xiao J, Harrison A, Lu L, Lin C, Jin D, Ho T. Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study. Nature Communications 2022;13(1) View
  37. Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallières M, Zhu S, Xie J, Peng Y, Iantsen A, Hatt M, Yuan Y, Ma J, Yang X, Rao C, Pai S, Ghimire K, Feng X, Naser M, Fuller C, Yousefirizi F, Rahmim A, Chen H, Wang L, Prior J, Depeursinge A. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Medical Image Analysis 2022;77:102336 View
  38. Baroudi H, Brock K, Cao W, Chen X, Chung C, Court L, El Basha M, Farhat M, Gay S, Gronberg M, Gupta A, Hernandez S, Huang K, Jaffray D, Lim R, Marquez B, Nealon K, Netherton T, Nguyen C, Reber B, Rhee D, Salazar R, Shanker M, Sjogreen C, Woodland M, Yang J, Yu C, Zhao Y. Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?. Diagnostics 2023;13(4):667 View
  39. Wahid K, Xu J, El-Habashy D, Khamis Y, Abobakr M, McDonald B, O’ Connell N, Thill D, Ahmed S, Sharafi C, Preston K, Salzillo T, Mohamed A, He R, Cho N, Christodouleas J, Fuller C, Naser M. Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy. Frontiers in Oncology 2022;12 View
  40. De Biase A, Sijtsema N, van Dijk L, Langendijk J, van Ooijen P. Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images. Physics in Medicine & Biology 2023;68(5):055013 View
  41. Beekman C, van Beek S, Stam J, Sonke J, Remeijer P. Improving predictive CTV segmentation on CT and CBCT for cervical cancer by diffeomorphic registration of a prior. Medical Physics 2022;49(3):1701 View
  42. Ma J, Zhang Y, Gu S, An X, Wang Z, Ge C, Wang C, Zhang F, Wang Y, Xu Y, Gou S, Thaler F, Payer C, Štern D, Henderson E, McSweeney D, Green A, Jackson P, McIntosh L, Nguyen Q, Qayyum A, Conze P, Huang Z, Zhou Z, Fan D, Xiong H, Dong G, Zhu Q, He J, Yang X. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge. Medical Image Analysis 2022;82:102616 View
  43. Sharkey M, Taylor J, Alabed S, Dwivedi K, Karunasaagarar K, Johns C, Rajaram S, Garg P, Alkhanfar D, Metherall P, O'Regan D, van der Geest R, Condliffe R, Kiely D, Mamalakis M, Swift A. Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Frontiers in Cardiovascular Medicine 2022;9 View
  44. de Vries L, Emmer B, Majoie C, Marquering H, Gavves E. PerfU-Net: Baseline infarct estimation from CT perfusion source data for acute ischemic stroke. Medical Image Analysis 2023;85:102749 View
  45. Podobnik G, Strojan P, Peterlin P, Ibragimov B, Vrtovec T. HaN‐Seg: The head and neck organ‐at‐risk CT and MR segmentation dataset. Medical Physics 2023;50(3):1917 View
  46. Asbach J, Singh A, Matott L, Le A. Deep learning tools for the cancer clinic: an open-source framework with head and neck contour validation. Radiation Oncology 2022;17(1) View
  47. Steybe D, Poxleitner P, Metzger M, Brandenburg L, Schmelzeisen R, Bamberg F, Tran P, Kellner E, Reisert M, Russe M. Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery 2022;17(11):2093 View
  48. Wahid K, Lin D, Sahin O, Cislo M, Nelms B, He R, Naser M, Duke S, Sherer M, Christodouleas J, Mohamed A, Murphy J, Fuller C, Gillespie E. Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites. Scientific Data 2023;10(1) View
  49. Lucido J, DeWees T, Leavitt T, Anand A, Beltran C, Brooke M, Buroker J, Foote R, Foss O, Gleason A, Hodge T, Hughes C, Hunzeker A, Laack N, Lenz T, Livne M, Morigami M, Moseley D, Undahl L, Patel Y, Tryggestad E, Walker M, Zverovitch A, Patel S. Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning. Frontiers in Oncology 2023;13 View
  50. Groendahl A, Huynh B, Tomic O, Søvik Å, Dale E, Malinen E, Skogmo H, Futsaether C. Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning. Frontiers in Veterinary Science 2023;10 View
  51. Zhong Y, Guo Y, Fang Y, Wu Z, Wang J, Hu W. Geometric and dosimetric evaluation of deep learning based auto‐segmentation for clinical target volume on breast cancer. Journal of Applied Clinical Medical Physics 2023;24(7) View
  52. Busch F, Xu L, Sushko D, Weidlich M, Truhn D, Müller-Franzes G, Heimer M, Niehues S, Makowski M, Hinsche M, Vahldiek J, Aerts H, Adams L, Bressem K. Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs. Computer Methods and Programs in Biomedicine 2023;234:107505 View
  53. Smolders A, Lomax A, Weber D, Albertini F. Patient-specific neural networks for contour propagation in online adaptive radiotherapy. Physics in Medicine & Biology 2023;68(9):095010 View
  54. Paudyal R, Shah A, Akin O, Do R, Konar A, Hatzoglou V, Mahmood U, Lee N, Wong R, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers 2023;15(9):2573 View
  55. Bakx N, van der Sangen M, Theuws J, Bluemink H, Hurkmans C. Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets. Technical Innovations & Patient Support in Radiation Oncology 2023;26:100209 View
  56. Song Y, Hu J, Wang Q, Yu C, Su J, Chen L, Jiang X, Chen B, Zhang L, Yu Q, Li P, Wang F, Bai S, Luo Y, Yi Z. Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor. Clinical and Translational Radiation Oncology 2023;41:100635 View
  57. Chen Y, Pahlavian S, Jacobs P, Neupane T, Forghani-Arani F, Castillo E, Castillo R, Vinogradskiy Y. Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database. International Journal of Radiation Oncology*Biology*Physics 2024;118(1):242 View
  58. Zaman F, Zhang L, Zhang H, Sonka M, Wu X. Segmentation quality assessment by automated detection of erroneous surface regions in medical images. Computers in Biology and Medicine 2023;164:107324 View
  59. Swain M, Ghosh-Laskar S, Patil R, Budrukkar A, Agarwal J. Precision radiation oncology in head and neck cancer: beyond physical precision - a narrative review. Journal of Cancer Metastasis and Treatment 2023 View
  60. Franzese C, Dei D, Lambri N, Teriaca M, Badalamenti M, Crespi L, Tomatis S, Loiacono D, Mancosu P, Scorsetti M. Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review. Journal of Personalized Medicine 2023;13(6):946 View
  61. Zhong N, Wang H, Huang X, Li Z, Cao L, Huo F, Liu B, Bu L. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Seminars in Cancer Biology 2023;95:52 View
  62. Vaassen F, Zegers C, Hofstede D, Wubbels M, Beurskens H, Verheesen L, Canters R, Looney P, Battye M, Gooding M, Compter I, Eekers D, van Elmpt W. Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology. Physica Medica 2023;114:103156 View
  63. Amjad A, Xu J, Thill D, Zhang Y, Ding J, Paulson E, Hall W, Erickson B, Li X. Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs. Frontiers in Oncology 2023;13 View
  64. Li J, Song Y, Wu Y, Liang L, Li G, Bai S. Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy. Frontiers in Oncology 2023;13 View
  65. Boussioux L, Ma Y, Thomas N, Bertsimas D, Shusharina N, Pursley J, Chen Y, DeLaney T, Qian J, Bortfeld T. Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble. International Journal of Radiation Oncology*Biology*Physics 2023;117(3):738 View
  66. Zaman F, Roy T, Sonka M, Wu X. Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks. Journal of Medical Imaging 2023;10(05) View
  67. Koo J, Caudell J, Latifi K, Moros E, Feygelman V. Essentially unedited deep‐learning‐based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning. Journal of Applied Clinical Medical Physics 2024;25(3) View
  68. Davey A, Pan S, Bryce-Atkinson A, Mandeville H, Janssens G, Kelly S, Hol M, Tang V, Davies L, SIOP-Europe Radiation Oncology Working Group , Aznar M. The need for consensus on delineation and dose constraints of dentofacial structures in paediatric radiotherapy: Outcomes of a SIOP Europe survey. Clinical and Translational Radiation Oncology 2023;43:100681 View
  69. Kesävuori R, Kaseva T, Salli E, Raivio P, Savolainen S, Kangasniemi M. Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection. European Radiology Experimental 2023;7(1) View
  70. Wang J, Peng Y. MHL-Net: A Multistage Hierarchical Learning Network for Head and Neck Multiorgan Segmentation. IEEE Journal of Biomedical and Health Informatics 2023;27(8):4074 View
  71. McQuinlan Y, Brouwer C, Lin Z, Gan Y, Sung Kim J, van Elmpt W, Gooding M. An investigation into the risk of population bias in deep learning autocontouring. Radiotherapy and Oncology 2023;186:109747 View
  72. Kovacs B, Netzer N, Baumgartner M, Schrader A, Isensee F, Weißer C, Wolf I, Görtz M, Jaeger P, Schütz V, Floca R, Gnirs R, Stenzinger A, Hohenfellner M, Schlemmer H, Bonekamp D, Maier-Hein K. Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer. Scientific Reports 2023;13(1) View
  73. Marin Anaya V. Artificial intelligence based auto-contouring solutions for use in radiotherapy treatment planning of head and neck cancer. IPEM-Translation 2023;6-8:100018 View
  74. Gay S, Cardenas C, Nguyen C, Netherton T, Yu C, Zhao Y, Skett S, Patel T, Adjogatse D, Guerrero Urbano T, Naidoo K, Beadle B, Yang J, Aggarwal A, Court L. Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy. Scientific Reports 2023;13(1) View
  75. Fathi Kazerooni A, Arif S, Madhogarhia R, Khalili N, Haldar D, Bagheri S, Familiar A, Anderson H, Haldar S, Tu W, Chul Kim M, Viswanathan K, Muller S, Prados M, Kline C, Vidal L, Aboian M, Storm P, Resnick A, Ware J, Vossough A, Davatzikos C, Nabavizadeh A. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study. Neuro-Oncology Advances 2023;5(1) View
  76. Li B, Zhang J, Wang Q, Li H, Wang Q. Three-dimensional spine reconstruction from biplane radiographs using convolutional neural networks. Medical Engineering & Physics 2024;123:104088 View
  77. Fernandes M, Bussink J, Wijsman R, Stam B, Monshouwer R. Estimating how contouring differences affect normal tissue complication probability modelling. Physics and Imaging in Radiation Oncology 2024;29:100533 View
  78. Prasad V, van Sloun R, Elzen S, Vilanova A, Pezzotti N. The Transform-and-Perform Framework: Explainable Deep Learning Beyond Classification. IEEE Transactions on Visualization and Computer Graphics 2024;30(2):1502 View
  79. Wang M, Yang R. Data-limited and imbalanced bladder wall segmentation with confidence map-guided residual networks via transfer learning. Frontiers in Physics 2024;11 View
  80. Walter A, Hoegen-Saßmannshausen P, Stanic G, Rodrigues J, Adeberg S, Jäkel O, Frank M, Giske K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers 2024;16(2):415 View
  81. Podobnik G, Ibragimov B, Peterlin P, Strojan P, Vrtovec T. vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images. Medical Physics 2024;51(3):2175 View
  82. Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq H, Wu Q, Yuan L, Xiao Y, Cai B, Latifi K, Benedict S, Buchsbaum J, Qi X. NRG Oncology Assessment of Artificial Intelligence Deep Learning–Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. International Journal of Radiation Oncology*Biology*Physics 2024;119(1):261 View
  83. Maroongroge S, Mohamed A, Nguyen C, Guma De la Vega J, Frank S, Garden A, Gunn B, Lee A, Mayo L, Moreno A, Morrison W, Phan J, Spiotto M, Court L, Fuller C, Rosenthal D, Netherton T. Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy. Physics and Imaging in Radiation Oncology 2024;29:100540 View
  84. Welch M, Kim S, Hope A, Huang S, Lu Z, Marsilla J, Kazmierski M, Rey‐McIntyre K, Patel T, O'Sullivan B, Waldron J, Bratman S, Haibe‐Kains B, Tadic T. RADCURE: An open‐source head and neck cancer CT dataset for clinical radiation therapy insights. Medical Physics 2024;51(4):3101 View
  85. Jinia A, Clarke S, Moran J, Pozzi S. Intelligent Radiation: A review of Machine learning applications in nuclear and radiological sciences. Annals of Nuclear Energy 2024;201:110444 View
  86. Nijkamp J. Challenges and chances for deep-learning based target and organ at risk segmentation in radiotherapy of head and neck cancer. Physics and Imaging in Radiation Oncology 2022;23:150 View
  87. Paudyal R, Jiang J, Han J, Diplas B, Riaz N, Hatzoglou V, Lee N, Deasy J, Veeraraghavan H, Shukla-Dave A. Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images. BJR|Artificial Intelligence 2024;1(1) View
  88. Huang Y, Yang J, Sun Q, Yuan Y, Li H, Hou Y. Multi-residual 2D network integrating spatial correlation for whole heart segmentation. Computers in Biology and Medicine 2024;172:108261 View
  89. Strasberg H, Jackson G, Bakken S, Boxwala A, Richardson J, Morrow J. Perspectives on the role of industry in informatics research and authorship. Journal of the American Medical Informatics Association 2024;31(5):1206 View
  90. Mody P, Huiskes M, Chaves-de-Plaza N, Onderwater A, Lamsma R, Hildebrandt K, Hoekstra N, Astreinidou E, Staring M, Dankers F. Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans. Physics and Imaging in Radiation Oncology 2024;30:100572 View
  91. Li C, Mao Y, Liang S, Li J, Wang Y, Guo Y. Deep causal learning for pancreatic cancer segmentation in CT sequences. Neural Networks 2024;175:106294 View
  92. Koitka S, Baldini G, Kroll L, van Landeghem N, Pollok O, Haubold J, Pelka O, Kim M, Kleesiek J, Nensa F, Hosch R. SAROS: A dataset for whole-body region and organ segmentation in CT imaging. Scientific Data 2024;11(1) View
  93. Nielsen C, Lorenzen E, Jensen K, Eriksen J, Johansen J, Gyldenkerne N, Zukauskaite R, Kjellgren M, Maare C, Lønkvist C, Nowicka-Matus K, Szejniuk W, Farhadi M, Ujmajuridze Z, Marienhagen K, Johansen T, Friborg J, Overgaard J, Hansen C. Interobserver variation in organs at risk contouring in head and neck cancer according to the DAHANCA guidelines. Radiotherapy and Oncology 2024;197:110337 View
  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;25(8) View
  102. Podobnik G, Ibragimov B, Tappeiner E, Lee C, Kim J, Mesbah Z, Modzelewski 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;198:110410 View
  103. Huang Y, Khodabakhshi Z, Gomaa A, Schmidt M, Fietkau R, Guckenberger M, Andratschke N, Bert C, Tanadini-Lang S, Putz F. Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation. Radiotherapy and Oncology 2024;198:110419 View
  104. Johnson C, Press R, Simone C, Shen B, Tsai P, Hu L, Yu F, Apinorasethkul C, Ackerman C, Zhai H, Lin H, Huang S. Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study. Frontiers in Oncology 2024;14 View
  105. Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial Intelligence in Pancreatic Image Analysis: A Review. Sensors 2024;24(14):4749 View
  106. Sjogreen C, Netherton T, Lee A, Soliman M, Gay S, Nguyen C, Mumme R, Vazquez I, Rhee D, Cardenas C, Martel M, Beadle B, Court L. Landmark‐based auto‐contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer. Journal of Applied Clinical Medical Physics 2024;25(9) View
  107. Wen F, Zhou J, Chen Z, Dou M, Yao Y, Wang X, Xu F, Shen Y. Efficient application of deep learning‐based elective lymph node regions delineation for pelvic malignancies. Medical Physics 2024;51(10):7057 View
  108. de Boisredon d’Assier M, Portafaix A, Vorontsov E, Le W, Kadoury S. Image-level supervision and self-training for transformer-based cross-modality tumor segmentation. Medical Image Analysis 2024;97:103287 View
  109. Bordigoni B, Trivellato S, Pellegrini R, Meregalli S, Bonetto E, Belmonte M, Castellano M, Panizza D, Arcangeli S, De Ponti E. Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models. Physica Medica 2024;125:104486 View
  110. Skett S, Patel T, Duprez D, Gupta S, Netherton T, Trauernicht C, Aldridge S, Eaton D, Cardenas C, Court L, Smith D, Aggarwal A. Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images. Physics and Imaging in Radiation Oncology 2024;31:100637 View
  111. Kim Y, Biggs S, Claridge Mackonis E. Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation. Physical and Engineering Sciences in Medicine 2024;47(3):1123 View
  112. Huynh B, Groendahl A, Tomic O, Liland K, Knudtsen I, Hoebers F, van Elmpt W, Dale E, Malinen E, Futsaether C. Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation. Biomedical Physics & Engineering Express 2024;10(5):055038 View
  113. Talcott W, Covington E, Bazan J, Wright J. The Future of Safety and Quality in Radiation Oncology. Seminars in Radiation Oncology 2024;34(4):433 View
  114. Rydygier M, Skóra T, Kisielewicz K, Spaleniak A, Garbacz M, Lipa M, Foltyńska G, Góra E, Gajewski J, Krzempek D, Kopeć R, Ruciński A. Proton Therapy Adaptation of Perisinusoidal and Brain Areas in the Cyclotron Centre Bronowice in Krakow: A Dosimetric Analysis. Cancers 2024;16(18):3128 View
  115. Wu C, Wu D, Zhu P. Non-destructive testing based on Unet-CBAM network for pulsed thermography. Frontiers in Physics 2024;12 View
  116. Nagayasu Y, Inui S, Ueda Y, Masaoka A, Tominaga M, Miyazaki M, Konishi K. Retrospective Comparison of Geometrical Accuracy among Atlas-based Auto-segmentation, Deep Learning Auto-segmentation, and Deformable Image Registration in the Treatment Replanning for Adaptive Radiotherapy of Head-and-Neck Cancer. Journal of Medical Physics 2024;49(3):335 View
  117. Zhang Y, Amjad A, Ding J, Sarosiek C, Zarenia M, Conlin R, Hall W, Erickson B, Paulson E. Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning. Practical Radiation Oncology 2024 View
  118. Kamel P, Khalid M, Steger R, Kanhere A, Kulkarni P, Parekh V, Yi P, Gandhi D, Bodanapally U. Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts. Journal of Imaging Informatics in Medicine 2024 View
  119. Murugesan G, McCrumb D, Aboian M, Verma T, Soni R, Memon F, Farahani K, Pei L, Wagner U, Fedorov A, Clunie D, Moore S, Van Oss J. AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons. Scientific Data 2024;11(1) View
  120. Simões R, Rijkmans E, Schaake E, Nowee M, van der Velden S, Janssen T. Evaluation of deep learning-based target auto-segmentation for Magnetic Resonance Imaging-guided cervix brachytherapy. Physics and Imaging in Radiation Oncology 2024;32:100669 View
  121. Constantinou A, Hoole A, Wong D, Sagoo G, Alvarez-Valle J, Takeda K, Griffiths T, Edwards A, Robinson A, Stubbington L, Bolger N, Rimmer Y, Elumalai T, Jayaprakash K, Benson R, Gleeson I, Sen R, Stockton L, Wang T, Brown S, Gatfield E, Sanghera C, Mourounas A, Evans B, Anthony A, Hou R, Toomey M, Wildschut K, Grisby A, Barnett G, McMullen R, Jena R. OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation. Clinical Oncology 2025;37:103660 View

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
  8. Fraile-Sanchón R, Vázquez-Ingelmo A, García-Peñalvo F, García-Holgado A. Proceedings of TEEM 2023. View
  9. Kalita A, Boruah A, Das T, Mazumder N, Jaiswal S, Zhuo G, Gogoi A, Kakoty N, Kao F. Biomedical Imaging. View
  10. Podobnik G, Vrtovec T. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. View
  11. Shi P, Hu J, Yang Y, Gao Z, Liu W, Ma T. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. View
  12. Podobnik G, Ocepek D, Škrlj L, Vrtovec T. Shape in Medical Imaging. View
  13. Yaushev F, Nogina D, Samokhin V, Dugova M, Petrash E, Sevryukov D, Belyaev M, Pisov M. Shape in Medical Imaging. View