- Poster presentation
- Open Access
Automatic detection of beating cilia with frequencies estimations
© Puybareau et al. 2015
- Published: 13 July 2015
- Single Sample
- Nasal Mucosa
- Automatic Detection
- Frequency Estimation
- Ciliated Cell
Muco-ciliary clearance is the airway first mechanism of defence against environmental attacks such as micro-organisms or pollution. Cilia motility impairment can be either of genetic (primary ciliary dyskinesia) or acquired origin (environmental attacks), entailing chronic diseases. It is of interest for practitioners to evaluate cilia beating frequency easily, robustly and reliably. As yet, no fully automatized method is available.
Ciliated cells were sampled in patients by brushing nasal mucosa and cilia beating was recorded using high speed video microscopy. We first estimated and removed the sensor pattern. We then stabilized the sequence assuming rigid transforms. We retained only the moving parts of the sequence and, after deblurring, characterized and segmented the moving parts in several regions of interest. The frequency was estimated for each region.
We output the processed sequence, a labeled mask of the various beating zones and a chart of the frequency observed in each region. Hence we obtained synchronization information between the different parts of the observed ciliated cells. An estimation of frequencies for each beating part is the final result.
With this new method, we propose a fully automatic estimation of cilia beating frequencies, which is able to deal with acquisition artifacts, such as sensor patterns, vibrations and noise, but also with the variety of frequencies we can observe on a single sample. We believe this may be a useful method for practitioners.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.