Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.
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[1] M. Essler,et al. Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy , 2023, Insights into Imaging.
[2] D. Conway,et al. Reviewing the epidemiology of head and neck cancer: definitions, trends and risk factors , 2022, British Dental Journal.
[3] E. Vokes,et al. Assessment of Tumor Burden and Response by RECIST vs. Volume Change in HPV+ Oropharyngeal Cancer – An Exploratory Analysis of Prospective Trials , 2022, International Journal of Radiation Oncology*Biology*Physics.
[4] M. Koizumi,et al. Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment. , 2022, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.
[5] A. French,et al. Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. , 2022, Radiology. Artificial intelligence.
[6] Ping Wang,et al. Combining the radiomics signature and HPV status for the risk stratification of patients with OPC. , 2022, Oral diseases.
[7] Joseph Nathanael Witanto,et al. Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning , 2022, Journal of magnetic resonance imaging : JMRI.
[8] Ziyan Wang,et al. Automated Measurement of Pancreatic Fat Deposition on Dixon MRI Using nnU‐Net , 2022, Journal of magnetic resonance imaging : JMRI.
[9] B. Menon,et al. Variability assessment of manual segmentations of ischemic lesion volume on 24-h non-contrast CT , 2021, Neuroradiology.
[10] Chris C. Duszynski,et al. Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke , 2021, Comput. Biol. Medicine.
[11] Se-Heon Kim,et al. Prediction of treatment outcome using MRI radiomics and machine learning in oropharyngeal cancer patients after surgical treatment. , 2021, Oral oncology.
[12] A. Madabhushi,et al. Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis , 2021, Frontiers in Oncology.
[13] G. Dot,et al. Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework , 2021, European Radiology.
[14] B. Jasperse,et al. Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning , 2021, Physics and imaging in radiation oncology.
[15] Yajia Gu,et al. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. , 2021, Magnetic resonance imaging.
[16] T. Wech,et al. Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction. , 2021, European journal of radiology.
[17] E. Dale,et al. Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[18] O. U. Aydin,et al. On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking , 2021, European Radiology Experimental.
[19] Erlend Hodneland,et al. Automated segmentation of endometrial cancer on MR images using deep learning , 2021, Scientific Reports.
[20] R. Savjani,et al. nnU-Net: Further Automating Biomedical Image Autosegmentation. , 2021, Radiology. Imaging cancer.
[21] Jens Petersen,et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.
[22] M. Bock,et al. Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis , 2020, Radiation Oncology.
[23] A. Demchuk,et al. Semi-automatic measurement of intracranial hemorrhage growth on non-contrast CT , 2019, International journal of stroke : official journal of the International Stroke Society.
[24] Xiaowei Ding,et al. Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..
[25] Clifton D Fuller,et al. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function. , 2018, International journal of radiation oncology, biology, physics.
[26] C. Fuller,et al. Large Interobserver Variation in the International MR-LINAC Oropharyngeal Carcinoma Delineation Study , 2017 .
[27] Christopher Rorden,et al. The first step for neuroimaging data analysis: DICOM to NIfTI conversion , 2016, Journal of Neuroscience Methods.
[28] E. Genden,et al. Oncologic Outcomes After Transoral Robotic Surgery: A Multi-institutional Study. , 2015, JAMA otolaryngology-- head & neck surgery.
[29] A. Chattopadhyay,et al. Oral cavity and oropharyngeal cancer incidence trends and disparities in the United States: 2000-2010. , 2015, Cancer epidemiology.
[30] D. Rischin,et al. What is the best treatment for patients with human papillomavirus–positive and –negative oropharyngeal cancer? , 2014, Cancer.
[31] Walter R. Bosch,et al. Multi-institutional trial of accelerated hypofractionated intensity-modulated radiation therapy for early-stage oropharyngeal cancer (RTOG 00-22). , 2010, International journal of radiation oncology, biology, physics.
[32] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[33] K. Shiga,et al. Differences between oral cancer and cancers of the pharynx and larynx on a molecular level. , 2012, Oncology letters.