AIMC Topic: Organs at Risk

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Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) a...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiometry Deep Learning Radiotherapy, Intensity-Modulated Humans Organs at Risk
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Advances in Auto-Segmentation.

Seminars in radiation oncology Jul 1, 2019
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to identify each patient's targets and anatomical structures. The efficacy and safety of the radiotherapy plan requires accurate segmentations as these regions of ...
Algorithms Neoplasms Neural Networks, Computer Software Organs at Risk Radiotherapy, Image-Guided Radiotherapy, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Humans
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The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

Technology in cancer research & treatment Jan 1, 2019
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience ...
Image Processing, Computer-Assisted Models, Theoretical Deep Learning Adolescent Organs at Risk Radiotherapy Planning, Computer-Assisted Nasopharyngeal Neoplasms Neoplasm Staging Neoplasm Grading Young Adult Adult Male Middle Aged Female Aged Algorithms Tomography, X-Ray Computed Radiotherapy, Image-Guided Humans
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Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Medical physics Feb 1, 2017
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs ...
Humans Neural Networks, Computer Software Radiotherapy Planning, Computer-Assisted Markov Chains Radiotherapy, Intensity-Modulated Observer Variation Head and Neck Neoplasms Organs at Risk Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Automated fluence map optimization based on fuzzy inference systems.

Medical physics Mar 1, 2016
PURPOSE: The planning of an intensity modulated radiation therapy treatment requires the optimization of the fluence intensities. The fluence map optimization (FMO) is many times based on a nonlinear continuous programming problem, being necessary fo...
Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Automation Fuzzy Logic Organs at Risk Humans Tomography, X-Ray Computed
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Models for predicting objective function weights in prostate cancer IMRT.

Medical physics Apr 1, 2015
PURPOSE: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate c...
Datasets as Topic Radiotherapy Dosage Logistic Models Organs at Risk Prostatic Neoplasms Radiotherapy Planning, Computer-Assisted Prostate Photons Rectum Retrospective Studies Radiotherapy, Intensity-Modulated Male Urinary Bladder Humans Machine Learning
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