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Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks

: Oyarzun Laura, Cristina; Hofmann, Patrick; Drechsler, Klaus; Wesarg, Stefan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Engineering in Medicine and Biology Society -EMBS-:
IEEE International Symposium on Biomedical Imaging, ISBI 2019 : April 8-11, 2019, Venice, Italy
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-5386-3640-4
ISBN: 978-1-5386-3641-1
ISBN: 978-1-5386-3642-8
International Symposium on Biomedical Imaging (ISBI) <16, 2019, Venice>
Conference Paper
Fraunhofer IGD ()
Lead Topic: Individual Health; Research Line: Computer vision (CV); medical imaging; medical image processing; object detection; Nasal cavity; Paranasal sinus; Organ detection

The nasal cavity and paranasal sinuses present large interpatient variabilities. Additional circumstances like for example, concha bullosa or nasal septum deviations complicate their segmentation. As in other areas of the body a previous multistructure 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.