A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

Medical Physics
Jinzhong YangMichalis Aristophanous

Abstract

To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the ...Continue Reading

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Nov 8, 2018·Physics in Medicine and Biology·Carlos E CardenasLaurence E Court
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Apr 28, 2019·Seminars in Radiation Oncology·Jolien Heukelom, Clifton David Fuller
Nov 29, 2020·Seminars in Nuclear Medicine·Kenneth JensenCai Grau

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