@inproceedings{11fc8b596ef642518e28571b52382630,
title = "Automatic detection of the nasal cavities and paranasal sinuses using deep neural networks",
abstract = "The nasal cavity and paranasal sinuses present large interpa-tient variabilities. Additional circumstances like for example, concha bullosa or nasal septum deviations complicate their segmentation. As in other areas of the body a previous multi-structure detection could facilitate the segmentation task. In this paper an approach is proposed to individually detect all sinuses and the nasal cavity. For a better delimitation of their borders the use of an irregular polyhedron is proposed. For an accurate prediction the Darknet-19 deep neural network is used which combined with the You Only Look Once method has shown very promising results in other fields of computer vision. 57 CT scans were available of which 85% were used for training and the remaining 15% for validation.",
keywords = "Deep learning, Nasal cavity, Organ detection, Paranasal sinus, YOLO",
author = "Laura, {Cristina Oyarzun} and Patrick Hofmann and Klaus Drechsler and Stefan Wesarg",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759481",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1154--1157",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
}