Oyarzun Laura, Cristina ; Hofmann, Patrick ; Drechsler, Klaus ; Wesarg, Stefan (2019)
Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks.
ISBI'19 - 16th International Symposium on Biomedical Imaging. Venice, Italy (08.-11.04.2019)
doi: 10.1109/ISBI.2019.8759481
Conference or Workshop Item, Bibliographie
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2019 |
| Creators: | Oyarzun Laura, Cristina ; Hofmann, Patrick ; Drechsler, Klaus ; Wesarg, Stefan |
| Type of entry: | Bibliographie |
| Title: | Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks |
| Language: | English |
| Date: | 2019 |
| Event Title: | ISBI'19 - 16th International Symposium on Biomedical Imaging |
| Event Location: | Venice, Italy |
| Event Dates: | 08.-11.04.2019 |
| DOI: | 10.1109/ISBI.2019.8759481 |
| URL / URN: | https://doi.org/10.1109/ISBI.2019.8759481 |
| 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. |
| Uncontrolled Keywords: | Medical imaging, Medical image processing, Object detection |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 10 Jul 2019 15:00 |
| Last Modified: | 09 Apr 2020 14:01 |
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