Oropharynx detection in PET-CT for tumor segmentation

Andrearczyk, Vincent (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Oreiller, Valentin (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland) ; Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland)

We propose an automatic detection of the oropharyngeal area in PET-CT images. This detection can be used to preprocess images for efficient segmentation of Head and Neck (H&N) tumors in the cropped regions by a Convolutional Neural Network (CNN) for treatment planning and large-scale radiomics studies (e.g. prognosis prediction). The developed method is based on simple image processing steps to segment the brain on the PET image and retrieve a fixed size bounding box of the extended oropharyngeal region. We evaluate the results by measuring whether the primary Gross Tumor Volume (GTV) is fully contained in the bounding box. 194 out of 201 regions (96.5%) are correctly detected. The code is available on our GitHub repository.


Keywords:
Conference Type:
short paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Sligo, Ireland, 31 August - 2 September 2020
Date:
2020-08
Sligo, Ireland
31 August - 2 September 2020
Pagination:
Pp. 109-112
Published in:
Proceedings of the 2020 Irish Machine Vision and Image Processing Conference (IMVIP 2020)
ISBN:
978-0-9934207-5-7
Appears in Collection:



 Record created 2021-01-11, last modified 2021-02-05

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