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Segmentation of organs‐at‐risks in head and neck <scp>CT</scp> images using convolutional neural networks

Medical Physics · 2016 · Vol. 44(2) · pp. 547–557

Bulat Ibragimov✉(Stanford University)Lei Xing(Stanford University)

Abstract

We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

Medical Imaging and Analysis Radiomics and Machine Learning in Medical Imaging Dental Radiography and ImagingConvolutional neural networkHead and neckSegmentationMedical imagingComputer scienceArtificial intelligenceImage segmentationComputer visionPattern recognition (psychology)Medicine

MeSH terms

Head and Neck NeoplasmsHumansImage Processing, Computer-AssistedMarkov ChainsRadiotherapy Planning, Computer-AssistedSoftwareTomography, X-Ray ComputedObserver VariationNeural Networks, ComputerRadiotherapy, Intensity-ModulatedOrgans at Risk