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  4. Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks
 
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2019
  • Konferenzbeitrag

Titel

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

Abstract
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.
Author(s)
Oyarzun Laura, Cristina
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hofmann, Patrick
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Drechsler, Klaus
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Wesarg, Stefan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE International Symposium on Biomedical Imaging, ISBI 2019
Konferenz
International Symposium on Biomedical Imaging (ISBI) 2019
Thumbnail Image
DOI
10.1109/ISBI.2019.8759481
Language
Englisch
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IGD
Tags
  • Lead Topic: Individua...

  • Research Line: Comput...

  • medical imaging

  • medical image process...

  • object detection

  • Nasal cavity

  • Paranasal sinus

  • Organ detection

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