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Home / Equine Research Bank / Sensors (Basel) / Three-Dimensional Segmentation of Equine Paranasal Sinuses i...
Sensors (Basel, Switzerland)2024; 24(11); 3538; doi: 10.3390/s24113538

Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with Clustering Analysis.

Abstract: The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.
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Publication Date: 2024-05-30 PubMed ID: 38894336PubMed Central: PMC11175080DOI: 10.3390/s24113538Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study developed a method to measure the size, shape, and density of different sinus cavities in horse heads using 3D CT imaging and analyzed how these features change with age using clustering techniques.

Study Background and Purpose

  • Equine paranasal sinuses consist of eight air-filled cavities arranged symmetrically on both sides of the horse’s head.
  • These sinuses are anatomically complex and difficult to study using traditional methods.
  • The goal was to use multidetector computed tomography (CT) to create three-dimensional segmentations of these sinuses for detailed morphometric measurements.
  • The study also aimed to explore whether age-related anatomical changes in these sinuses can be identified through computer-assisted clustering analysis.

Methods

  • Sample: Heads from 18 cadaver horses aged between 2 to 25 years were imaged using CT scans.
  • Segmentation: Each sinus cavity was segmented to isolate and extract data on volume, surface area, and relative density.
  • Sinuses analyzed included:
    • Frontal sinus (FS)
    • Dorsal conchal sinus (DCS)
    • Ventral conchal sinus (VCS)
    • Rostral maxillary sinus (RMS)
    • Caudal maxillary sinus (CMS)
    • Sphenoid sinus (SS)
    • Palatine sinus (PS)
    • Middle conchal sinus (MCS)
  • Age groupings: The horses were categorized into young, middle-aged, and old groups.
  • Clustering Analysis: K-means clustering was applied to the morphometric data to classify sinuses based on age-related characteristics.

Key Findings

  • Morphometric differences were present depending on:
    • Sinus position (which sinus was measured)
    • Age of the horse
  • No significant differences were found between the left and right sides of the head, indicating symmetry in sinus morphology.
  • The volume and surface area of three sinuses—the ventral conchal sinus (VCS), rostral maxillary sinus (RMS), and caudal maxillary sinus (CMS)—tended to increase with age.
  • Using K-means clustering:
    • The rostral maxillary sinus (RMS) showed good age classification accuracy (0.72), indicating morphometric measures here can help distinguish age groups.
    • The caudal maxillary sinus (CMS) showed moderate accuracy (0.67) for age-related clustering.
    • The ventral conchal sinus (VCS) showed poor clustering accuracy (0.31), suggesting less reliable age-related morphometric variation.

Implications and Conclusions

  • This research demonstrates the feasibility of using 3D CT segmentation combined with morphometric analysis for detailed study of equine sinus anatomy.
  • It identifies that specific sinuses change in size and surface area as horses age, reflecting age-related anatomical remodeling.
  • Computer-assisted clustering analysis can effectively differentiate age groups based on measurements from some sinuses, particularly RMS and CMS.
  • The approach may offer clinical and research benefits for diagnosing sinus-related conditions in horses and understanding how sinus morphology evolves with age.
  • Further studies with larger samples could refine these models and validate the utility of such computer-aided segmentation and classification methods.

Cite This Article

APA
Borowska M, Lipowicz P, Daunoravičienė K, Turek B, Jasiński T, Pauk J, Domino M. (2024). Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with Clustering Analysis. Sensors (Basel), 24(11), 3538. https://doi.org/10.3390/s24113538

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 24
Issue: 11
PII: 3538

Researcher Affiliations

Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Lipowicz, Paweł
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Daunoravičienė, Kristina
  • Department of Biomechanical Engineering, Vilnius Gediminas Technical University, 03224 Vilnius, Lithuania.
Turek, Bernard
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Pauk, Jolanta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.

MeSH Terms

  • Horses
  • Animals
  • Cluster Analysis
  • Paranasal Sinuses / diagnostic imaging
  • Imaging, Three-Dimensional / methods
  • Multidetector Computed Tomography / methods
  • Algorithms

Conflict of Interest Statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.

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Citations

This article has been cited 3 times.
  1. Kozłowska N, Borowska M, Jasiński T, Wierzbicka M, Domino M. Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images.. Animals (Basel) 2025 Sep 22;15(18).
    doi: 10.3390/ani15182758pubmed: 41008003google scholar: lookup
  2. Riahi Z, Naeini AT, Zare R. Evaluation of maturation-related changes in maxillary sinus diameter and cheek teeth positioning relative to the maxillary sinus in the Dareshuri horse.. J Equine Sci 2025;36(3):81-91.
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  3. Turek B, Pawlikowski M, Jankowski K, Borowska M, Skierbiszewska K, Jasiński T, Domino M. Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm.. BMC Vet Res 2025 Mar 13;21(1):165.
    doi: 10.1186/s12917-025-04591-5pubmed: 40082938google scholar: lookup
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