FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sensors (Basel) / Three-Dimensional Segmentation Assisted with Clustering Anal...
Sensors (Basel, Switzerland)2023; 23(21); 8940; doi: 10.3390/s23218940

Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets.

Abstract: Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses' heads and to extract volumetric features. Axial vertical symmetry, but not horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors' equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches.
Get Full Text
Publication Date: 2023-11-02 PubMed ID: 37960639PubMed Central: PMC10650163DOI: 10.3390/s23218940Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

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.

This study investigates the differences in the surface area and volume of horses’ teeth using 3D images obtained from a commonly used CT scanning method. With the help of software, the researchers made and analyzed 3D models of each individual tooth from 50 horses to determine variations in size and volume relating to tooth growth, position in the mouth, and a specific type of dental disease.

Methods and Analysis

  • The study focused on advanced dental diagnostic imaging technology, specifically multidetector computed tomography (CT).
  • The team used CT scans to create 3D models and to segment and measure distinct parts of equine or horse incisors (front teeth).
  • A software called 3D Slicer was used to segment the teeth and extract volumetric features.
  • The team analyzed whether any axial (horizontal or vertical) symmetry was present in the incisors.

Findings

  • Results indicated a vertical symmetry of the horse incisors, but not horizontal.
  • Significant differences were observed in the surface area and volume between temporary (baby teeth) and permanent incisors in horses.
  • These differences allowed for the eruption-related cluster analysis of the 3D images with a high degree of accuracy (over 75%).
  • There were also slight differences in the volumetric features between incisors at the center, between the center and the corner (intermediate), and at the corners of the horse mouth.
  • These differences led to a moderate level of location-related cluster accuracy (over 69%).

Dental Disease Comparison

  • The team compared the volumetric features of diseased mandibular (lower jaw) incisors afflicted with equine odontoclastic tooth resorption and hypercementosis (EOTRH) to those of maxillary (upper jaw) incisors.
  • The mandibular incisors affected by EOTRH showed greater volumetric features than the maxillary incisors.
  • As a result, the accuracy of EOTRH degree-related clustering was higher for the mandibular (over 72%) as compared to the maxillary (over 33%).

Conclusion

  • By using commonly obtained multidetector CT data sets, the study successfully segmented CT-based 3D images of equine incisors.
  • The proposed data-processing approaches have valuable clinical applications in identifying and assessing dental disease in horses.

Cite This Article

APA
Borowska M, Jasiński T, Gierasimiuk S, Pauk J, Turek B, Górski K, Domino M. (2023). Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets. Sensors (Basel), 23(21), 8940. https://doi.org/10.3390/s23218940

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 23
Issue: 21
PII: 8940

Researcher Affiliations

Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Gierasimiuk, Sylwia
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Pauk, Jolanta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Turek, Bernard
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Górski, Kamil
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, 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
  • Incisor / diagnostic imaging
  • Multidetector Computed Tomography
  • Tooth Resorption / veterinary
  • Hypercementosis / veterinary
  • Cluster Analysis
  • Maxilla

Grant Funding

  • Miniatura 6 No 2022/06/X/ST6/00431 / National Science Center
  • WI/WM-IIB/2/2021 / Polish Ministry of Science and Higher Education

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 46 references
  1. Jinga MR, Lee RB, Chan KL, Marway PS, Nandapalan K, Rhode K, Kui C, Lee M. Assessing the impact of 3D image segmentation workshops on anatomical education and image interpretation: A prospective pilot study.. Anat. Sci. Educ. 2023;16:1024–1032.
    doi: 10.1002/ase.2314pubmed: 37381649google scholar: lookup
  2. Kim E, Fischetti AJ, Sreetharan P, Weltman JG, Fox PR. Comparison of artificial intelligence to the veterinary radiologist’s diagnosis of canine cardiogenic pulmonary edema.. Vet. Radiol. Ultrasound. 2022;63:292–297.
    doi: 10.1111/vru.13062pubmed: 35048445google scholar: lookup
  3. Mandolini M, Brunzini A, Facco G, Mazzoli A, Forcellese A, Gigante A. Comparison of three 3D segmentation software tools for hip surgical planning.. Sensors 2022;22:5242.
    doi: 10.3390/s22145242pmc: PMC9323631pubmed: 35890923google scholar: lookup
  4. Biedrzycki AH, Morton AJ, Perez-Jimenez EE, Elane GL, Roe HA, Trolinger-Meadows KD. Three-dimensional printed surgical guides for keratoma removal in horses using computed tomography or magnetic resonance imaging-based segmentation.. Vet. Surg. 2022;51:O43–O52.
    doi: 10.1111/vsu.13786pubmed: 35225367google scholar: lookup
  5. Kau S, Failing K, Staszyk C. Computed tomography (CT)-assisted 3D cephalometry in horses: Interincisal angulation of clinical crowns.. Front. Vet. Sci. 2020;7:434.
    doi: 10.3389/fvets.2020.00434pmc: PMC7403475pubmed: 32851019google scholar: lookup
  6. Listmann L, Schrock P, Failing K, Staszyk C. Occlusal angles of equine incisors.. J. Vet. Dent. 2017;34:259–267.
    doi: 10.1177/0898756417739465pubmed: 29130414google scholar: lookup
  7. Herren FL, Gerber V, Meier R, Schweizer-Gorgas D, Klopfenstein Bregger MD. Semi-Automatic Segmentation of Cone Beam Computed Tomography Datasets for Volume Measurements of Equine Cheek Teeth.. J. Vet. Dent. 2022;39:41–48.
    doi: 10.1177/08987564211061630pubmed: 34866465google scholar: lookup
  8. Kozłowska N, Wierzbicka M, Jasiński T, Domino M. Advances in the Diagnosis of Equine Respiratory Diseases: A Review of Novel Imaging and Functional Techniques.. Animals 2022;12:381.
    doi: 10.3390/ani12030381pmc: PMC8833607pubmed: 35158704google scholar: lookup
  9. Fenner M, Verwilghen D, Townsend N, Simhofer H, Schwarzer J, Zani D, Bienert-Zeit A. Paranasal sinus cysts in the horse: Complications related to their presence and surgical treatment in 37 cases.. Equine Vet. J. 2019;51:57–63.
    doi: 10.1111/evj.12959pubmed: 29679404google scholar: lookup
  10. Dixon PM, Barnett TP, Morgan RE, Reardon RJ. Computed tomographic assessment of individual paranasal sinus compartment and nasal conchal bulla involvement in 300 cases of equine sinonasal disease.. Front. Vet. Sci. 2020;7:580356.
    doi: 10.3389/fvets.2020.580356pmc: PMC7652901pubmed: 33195594google scholar: lookup
  11. Casey M, Pearson G, Perkins J, Tremaine W. Gross, computed tomographic and histological findings in mandibular cheek teeth extracted from horses with clinical signs of pulpitis due to apical infection.. Equine Vet. J. 2015;47:557–567.
    doi: 10.1111/evj.12315pubmed: 24975383google scholar: lookup
  12. Horbal A, Smith S, Dixon PM. A computed tomographic (CT) and pathological study of equine cheek teeth infundibulae extracted from asymptomatic horses. Part 1: Prevalence, type and location of infundibular lesions on CT imaging.. Front. Vet. Sci. 2019;6:124.
    doi: 10.3389/fvets.2019.00124pmc: PMC6494954pubmed: 31106213google scholar: lookup
  13. Henninger W, Mairi Frame E, Willmann M, Simhofer H, Malleczek D, Kneissl SM, Mayrhofer E. CT features of alveolitis and sinusitis in horses.. Vet. Radiol. Ultrasound. 2003;44:269–276.
    doi: 10.1111/j.1740-8261.2003.tb00454.xpubmed: 12816367google scholar: lookup
  14. Selberg K, Easley JT. Advanced imaging in equine dental disease.. Vet. Clin. Equine Pract. 2013;29:397–409.
    doi: 10.1016/j.cveq.2013.04.009pubmed: 23915666google scholar: lookup
  15. Manso-Díaz G, García-López J, Maranda L, Taeymans O. The role of head computed tomography in equine practice.. Equine Vet. Educ. 2015;27:136–145.
    doi: 10.1111/eve.12275google scholar: lookup
  16. Stieger-Vanegas SM, Hanna AL. The role of computed tomography in imaging non-neurologic disorders of the head in equine patients.. Front. Vet. Sci. 2022;9:798216.
    doi: 10.3389/fvets.2022.798216pmc: PMC8936190pubmed: 35321060google scholar: lookup
  17. Dixon P, Tremaine W, Pickles K, Kuhns L, Hawe C, McCann J, McGorum B, Railton D, Brammer S. Equine dental disease part 1: A long-term study of 400 cases: Disorders of incisor, canine and first premolar teeth.. Equine Vet. J. 1999;31:369–377.
    doi: 10.1111/j.2042-3306.1999.tb03835.xpubmed: 10505951google scholar: lookup
  18. Dixon P, Tremaine W, Pickles K, Kuhns L, Hawe C, Mccann J, McGorum B, Railton D, Brammer S. Equine dental disease Part 3: A long-term study of 400 cases: Disorders of wear, traumatic damage and idiopathic fractures, tumours and miscellaneous disorders of the cheek teeth.. Equine Vet. J. 2000;32:9–18.
    doi: 10.2746/042516400777612099pubmed: 10661379google scholar: lookup
  19. Staszyk C, Bienert A, Kreutzer R, Wohlsein P, Simhofer H. Equine odontoclastic tooth resorption and hypercementosis.. Vet. J. 2008;178:372–379.
    doi: 10.1016/j.tvjl.2008.09.017pubmed: 19010701google scholar: lookup
  20. Górski K, Stefanik E, Turek B, Bereznowski A, Czopowicz M, Polkowska I, Domino M. Malocclusions and Dental Diseases in Privately Owned Horses in the Mazovia Region of Poland.. Animals 2022;12:3120.
    doi: 10.3390/ani12223120pmc: PMC9686654pubmed: 36428347google scholar: lookup
  21. Dixon PM, Kennedy R, Reardon RJ. Equine “idiopathic” and infundibular caries-related cheek teeth fractures: A long-term study of 486 fractured teeth in 300 horses.. Front. Vet. Sci. 2021;8:646870.
    doi: 10.3389/fvets.2021.646870pmc: PMC8192706pubmed: 34124217google scholar: lookup
  22. Liuti T, Smith S, Dixon P. Radiographic, computed tomographic, gross pathological and histological findings with suspected apical infection in 32 equine maxillary cheek teeth (2012–2015). Equine Vet. J. 2018;50:41–47.
    doi: 10.1111/evj.12729pubmed: 28772346google scholar: lookup
  23. Earley E, Rawlinson JT. A new understanding of oral and dental disorders of the equine incisor and canine teeth.. Vet. Clin. Equine Pract. 2013;29:273–300.
    doi: 10.1016/j.cveq.2013.04.011pubmed: 23915661google scholar: lookup
  24. Staszyk C, Suske A, Pöschke A. Equine dental and periodontal anatomy: A tutorial review.. Equine Vet. Educ. 2015;27:474–481.
    doi: 10.1111/eve.12317google scholar: lookup
  25. DeLorey MS. A retrospective evaluation of 204 diagonal incisor malocclusion corrections in the horse.. J. Vet. Dent. 2007;24:145–149.
    doi: 10.1177/089875640702400302pubmed: 17985690google scholar: lookup
  26. Loch W.E., Bradley M. Determining Age of Horses by Their Teeth. 1998. [(accessed on 1 September 2023)]. Available online: https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/4028/DeterminingAgeHorsesByTeeth.pdf?sequence=3&isAllowed=y.
  27. Muylle S, Simoens P, Lauwers H. Age-related morphometry of equine incisors.. J. Vet. Med. Ser. A. 1999;46:633–643.
    doi: 10.1046/j.1439-0442.1999.00261.xpubmed: 10638301google scholar: lookup
  28. Brounts SH, Henry T, Lund JR, Whitton RC, Ergun DL, Muir P. Use of a novel helical fan beam imaging system for computed tomography of the head and neck in sedated standing horses: 120 cases (2019–2020). J. Am. Vet. Med Assoc. 2022;260:1361–1368.
    doi: 10.2460/javma.21.10.0471pubmed: 35943950google scholar: lookup
  29. Górski K, Borowska M, Stefanik E, Polkowska I, Turek B, Bereznowski A, Domino M. Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model.. Biomedicines 2022;10:2914.
    doi: 10.3390/biomedicines10112914pmc: PMC9687516pubmed: 36428482google scholar: lookup
  30. Floyd MR. The modified Triadan system: Nomenclature for veterinary dentistry.. J. Vet. Dent. 1991;8:18–19.
    doi: 10.1177/089875649100800402pubmed: 1815632google scholar: lookup
  31. Hüls I, Bienert A, Staszyk C. Equine odontoclastic tooth resorption and hyper-cementosis (EOTRH): Röntgenologische und makroskopisch-anatomische Befunde; Proceedings of the 10 Jahrestagung der Internationalen Gesellschaft zur Funktionsverbesserung der Pferdezähne; Wiesbaden, Germany.. 3–4 March 2012; Mühltal, Germany: Internationale Gesellschaft zur Funktionsverbesserung der Pferdezähne; 2012.
  32. Rehrl S, Schröder W, Müller C, Staszyk C, Lischer C. Radiological prevalence of equine odontoclastic tooth resorption and hypercementosis.. Equine Vet. J. 2018;50:481–487.
    doi: 10.1111/evj.12776pubmed: 29067719google scholar: lookup
  33. Górski K, Borowska M, Stefanik E, Polkowska I, Turek B, Bereznowski A, Domino M. Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses’ Incisor Teeth Affected by the EOTRH Syndrome.. Sensors 2022;22:2920.
    doi: 10.3390/s22082920pmc: PMC9030967pubmed: 35458905google scholar: lookup
  34. Górski K, Borowska M, Turek B, Pawlikowski M, Jankowski K, Bereznowski A, Polkowska I, Domino M. An application of the density standard and scaled–pixel–counting protocol to assess the radiodensity of equine incisor teeth affected by resorption and hypercementosis: Preliminary advancement in dental radiography.. BMC Vet. Res. 2023;19:116.
    doi: 10.1186/s12917-023-03675-4pmc: PMC10413604pubmed: 37559089google scholar: lookup
  35. Brigham WH. 3D Slicer Image Computing Platform.. 3D Slicer 2019. v4.10.
  36. Lo Giudice A, Ronsivalle V, Grippaudo C, Lucchese A, Muraglie S, Lagravère MO, Isola G. One step before 3D printing—Evaluation of imaging software accuracy for 3-dimensional analysis of the mandible: A comparative study using a surface-to-surface matching technique.. Materials 2020;13:2798.
    doi: 10.3390/ma13122798pmc: PMC7345160pubmed: 32575875google scholar: lookup
  37. Schrock P, Lüpke M, Seifert H, Staszyk C. Three-dimensional anatomy of equine incisors: Tooth length, enamel cover and age related changes.. BMC Vet. Res. 2013;9:249.
    doi: 10.1186/1746-6148-9-249pmc: PMC3878928pubmed: 24321365google scholar: lookup
  38. Henry T, Puchalski S, Arzi B, Kass P, Verstraete F. Radiographic evaluation in clinical practice of the types and stage of incisor tooth resorption and hypercementosis in horses.. Equine Vet. J. 2017;49:486–492.
    doi: 10.1111/evj.12650pubmed: 27862232google scholar: lookup
  39. Lorello O, Foster D, Levine D, Boyle A, Engiles J, Orsini J. Clinical treatment and prognosis of equine odontoclastic tooth resorption and hypercementosis.. Equine Vet. J. 2016;48:188–194.
    doi: 10.1111/evj.12406pubmed: 25557855google scholar: lookup
  40. Schrock P, Lüpke M, Seifert H, Staszyk C. Finite element analysis of equine incisor teeth. Part 2: Investigation of stresses and strain energy densities in the periodontal ligament and surrounding bone during tooth movement.. Vet. J. 2013;198:590–598.
    doi: 10.1016/j.tvjl.2013.10.010pubmed: 24252223google scholar: lookup
  41. Ghazal TM. Performances of K-means clustering algorithm with different distance metrics.. Intell. Autom. Soft Comput. 2021;30:735–742.
    doi: 10.32604/iasc.2021.019067google scholar: lookup
  42. Eelbode T, Bertels J, Berman M, Vandermeulen D, Maes F, Bisschops R, Blaschko MB. Optimization for medical image segmentation: Theory and practice when evaluating with dice score or jaccard index.. IEEE Trans. Med. Imaging. 2020;39:3679–3690.
    doi: 10.1109/TMI.2020.3002417pubmed: 32746113google scholar: lookup
  43. Belém MDF, Ambrosano GMB, Tabchoury CPM, Ferreira-Santos RI, Haiter-Neto F. Performance of digital radiography with enhancement filters for the diagnosis of proximal caries.. Braz. Oral Res. 2013;27:245–251.
    doi: 10.1590/S1806-83242013000300004pubmed: 23739784google scholar: lookup
  44. Geetha V, Aprameya K. Textural analysis based classification of digital X-ray images for dental caries diagnosis.. Int. J. Eng. Manuf. (IJEM) 2019;9:44–45.
  45. Martelli N, Serrano C, van den Brink H, Pineau J, Prognon P, Borget I, El Batti S. Advantages and disadvantages of 3-dimensional printing in surgery: A systematic review.. Surgery 2016;159:1485–1500.
    doi: 10.1016/j.surg.2015.12.017pubmed: 26832986google scholar: lookup
  46. Chae MP, Chung RD, Smith JA, Hunter-Smith DJ, Rozen WM. The accuracy of clinical 3D printing in reconstructive surgery: Literature review and in vivo validation study.. Gland Surg. 2021;10:2293.
    doi: 10.21037/gs-21-264pmc: PMC8340329pubmed: 34422600google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.