New method for estimating the volume and volume fractions of the nasal structures in the goose (Anser anser domesticus) using computed tomography images


Onuk B., Kabak M., Sahin B., Ince N., Selcuk M. B.

BRITISH POULTRY SCIENCE, vol.54, no.4, pp.441-446, 2013 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 54 Issue: 4
  • Publication Date: 2013
  • Doi Number: 10.1080/00071668.2013.806980
  • Journal Name: BRITISH POULTRY SCIENCE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.441-446

Abstract

1. The conchae within the nasal cavity of poultry are important for water and energy conservation, but have not been experimentally evaluated. The aim of the present study was to determine the accuracy of volume and volume fraction estimates of the conchae, nasal septum and nasal cavity. 2. The nasal cavities of 7 adult goose heads were scanned using computed tomography (CT), with images sampled randomly at a 1/5 sampling fraction. Physical sections were obtained from the same samples, using an electric saw that had an adjustable section range, and provided 14 to 15 sections with a thickness of 2.5 mm. The section surface areas of the nasal cavity, nasal septum and conchae were estimated using the Cavalieri principle. Results obtained using the CT and physical section images were compared. Volumes and volume fractions obtained from the physical sections were accepted as the gold standard and differences in the CT images were determined. 3. Multiplication of the data obtained on the CT images with the deviation percentage of the physical sections produced normalised values. No differences were observed between the gold standard data and the CT images. While it was possible to normalise the obtained data using the gold standard values, the raw data could also be used for comparative studies because the deviations from normal would be similar for all groups. 4. Our study showed that the nasal structures could be estimated in vivo using CT images.