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

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 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 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 2022 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 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 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

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