FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Front Vet Sci / EquiSim: An Open-Source Articulatable Statistical Model of t...
Frontiers in veterinary science2021; 8; 623318; doi: 10.3389/fvets.2021.623318

EquiSim: An Open-Source Articulatable Statistical Model of the Equine Distal Limb.

Abstract: Most digital models of the equine distal limb that are available in the community are static and/or subject specific; hence, they have limited applications in veterinary research. In this paper, we present an articulatable model of the entire equine distal limb based on statistical shape modeling. The model describes the inter-subject variability in bone geometry while maintaining proper jointspace distances to support model articulation toward different poses. Shape variation modes are explained in terms of common biometrics in order to ease model interpretation from a veterinary point of view. The model is publicly available through a graphical user interface (https://github.com/jvhoutte/equisim) in order to facilitate future digitalization in veterinary research, such as computer-aided designs, three-dimensional printing of bone implants, bone fracture risk assessment through finite element methods, and data registration and segmentation problems for clinical practices.
Copyright © 2021 Van Houtte, Vandenberghe, Zheng, Huysmans and Sijbers.
Get Full Text
Publication Date: 2021-03-03 PubMed ID: 33763462PubMed Central: PMC7982960DOI: 10.3389/fvets.2021.623318Google 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 research introduces ‘EquiSim’, an open-source, articulatable digital model of the horse’s lower limb that can be varied based on statistical shape modeling. The model, which considers the diverse bone geometries of different horses, aims to assist future digitalization in veterinary research, such as computer-aided designs and 3D printing of bone implants.

Problem with Existing Models

Existing digital models of the equine distal limb (lower part of a horse’s limb) typically exhibit two key limitations:

  • They are static: This means they do not adapt or change in any way, limiting their overall utility and adaptability.
  • They are subject-specific: These models focus on representations of the limbs of individual horses and do not adequately represent differences in bone geometry between different equine subjects.

The EquiSim Model

The EquiSim model is a significant advancement in the field of veterinary research for several reasons:

  • It is based on statistical shape modeling: This approach means the model upholds the bone geometry variances that exist between different horses.
  • It maintains proper joint space distances: Ensuring these distances are correct in the model allows it to imitate different poses accurately.
  • The model uses common biometrics to explain shape variation modes, making it easier for it to be interpreted from a veterinary perspective,

Applications of EquiSim

EquiSim opens up a range of possibilities for digitization in veterinary research and practice, including:

  • Computer-aided designs: EquiSim can be used to design and refine equipment and implements that interact with the equine distal limb in a more accurate and refined manner.
  • Three-dimensional printing of bone implants: The model’s ability to represent different horse bone geometries accurately makes it useful in the design of 3D printed bone implants, aiding in more successful surgical outcomes.
  • Bone fracture risk assessment through finite element methods: EquiSim can contribute to risk assessments related to potential bone fractures.
  • Data registration and segmentation problems for clinical practices: The model can assist in solving these technical issues, improving veterinary clinical practices.

The researchers have made the EquiSim model publicly available through a user-friendly graphical interface. This means it can be accessed and used by a wide range of researchers and practitioners in the veterinary field, driving further innovations and improvements in equine health and care.

Cite This Article

APA
Van Houtte J, Vandenberghe F, Zheng G, Huysmans T, Sijbers J. (2021). EquiSim: An Open-Source Articulatable Statistical Model of the Equine Distal Limb. Front Vet Sci, 8, 623318. https://doi.org/10.3389/fvets.2021.623318

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 8
Pages: 623318
PII: 623318

Researcher Affiliations

Van Houtte, Jeroen
  • imec-Vision Lab, University of Antwerp, Antwerp, Belgium.
Vandenberghe, Filip
  • Equine Hospital Bosdreef, Moerbeke-Waas, Belgium.
Zheng, Guoyan
  • Center for Image-Guided Therapy and Interventions, Institute for Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
Huysmans, Toon
  • imec-Vision Lab, University of Antwerp, Antwerp, Belgium.
  • Section on Applied Ergonomics and Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.
Sijbers, Jan
  • imec-Vision Lab, University of Antwerp, Antwerp, Belgium.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

This article includes 44 references
  1. Zarucco L, Wisner ER, Swanstrom MD, Stover SM. Image fusion of computed tomographic and magnetic resonance images for the development of a three-dimensional musculoskeletal model of the equine forelimb.. Vet Radiol Ultrasound 2006 Oct-Nov;47(6):553-62.
    doi: 10.1111/j.1740-8261.2006.00185.xpubmed: 17153064google scholar: lookup
  2. Harrison SM, Whitton RC, Kawcak CE, Stover SM, Pandy MG. Relationship between muscle forces, joint loading and utilization of elastic strain energy in equine locomotion.. J Exp Biol 2010 Dec 1;213(Pt 23):3998-4009.
    doi: 10.1242/jeb.044545pubmed: 21075941google scholar: lookup
  3. Becker J, Emmanuel M, Jean-Marc L. Joint loading estimation method for horse forelimb high jerk locomotion: jumping.. J Bionic Eng (2019) 16:674–85.
    doi: 10.1007/s42235-019-0054-zgoogle scholar: lookup
  4. Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS. Accuracy Quantification of the Reverse Engineering and High-Order Finite Element Analysis of Equine MC3 Forelimb.. J Equine Vet Sci 2019 Jul;78:94-106.
    doi: 10.1016/j.jevs.2019.04.004pubmed: 31203991google scholar: lookup
  5. Lee S, Lee J, Lee A, Park N, Lee S, Song S, Seo A, Lee H, Kim JI, Eom K. Augmented reality intravenous injection simulator based 3D medical imaging for veterinary medicine.. Vet J 2013 May;196(2):197-202.
    doi: 10.1016/j.tvjl.2012.09.015pubmed: 23103217google scholar: lookup
  6. Van Houtte J, Bazrafkan S, Vandenberghe F, Zheng G, Sijbers J. A deep learning approach to horse bone segmentation from digitally reconstructed radiographs.. 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA) (2019) p. 1–6.
    doi: 10.1109/IPTA.2019.8936082google scholar: lookup
  7. Dzierzçka M, Purzyc H, Charuta A, Barszcz K, Komosa M, Hecold M. Evaluation of distal phalanx formation and association with front hoof conformation in coldblooded horses.. Biologia (2016) 71:337–42.
    doi: 10.1515/biolog-2016-0037google scholar: lookup
  8. Faramarzi B, McMicking H, Halland S, Kaneps A, Dobson H. Incidence of palmar process fractures of the distal phalanx and association with front hoof conformation in foals.. Equine Vet J 2015 Nov;47(6):675-9.
    doi: 10.1111/evj.12375pubmed: 25297555google scholar: lookup
  9. Faramarzi B, Kepler A, Dong F, Dobson H. Morphovolumetric analysis of the hoof in standardbred horses.. J Equine Vet Sci (2018) 71:40–5.
    doi: 10.1016/j.jevs.2018.08.012google scholar: lookup
  10. Souza A, Kunz J, Laus R, Moreira M, Muller T, Fonteque J. Biometrics of hoof balance in equids.. Arquivo Brasil Med Vet Zoot (2016) 68:825–31.
    doi: 10.1590/1678-4162-8848google scholar: lookup
  11. Sampaio BFB, Zúccari CESN, Shiroma MYM, Bertozzo BR, Leonel ECR, Surjus RdS. Biometric hoof evaluation of athletic horses of show jumping, barrel, long rope and polo modalities.. Rev Brasil Saúde Prod Anim (2013) 14:448–59.
    doi: 10.1590/S1519-99402013000300017google scholar: lookup
  12. Alrtib AM, Philip CJ, Abdunnabi AH, Davies HM. Morphometrical study of bony elements of the forelimb fetlock joints in horses.. Anat Histol Embryol 2013 Feb;42(1):9-20.
    doi: 10.1111/j.1439-0264.2012.01158.xpubmed: 22571501google scholar: lookup
  13. Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models-their training and application.. Comput Vis Image Understand (1995) 61:38–59.
    doi: 10.1006/cviu.1995.1004google scholar: lookup
  14. Audenaert EA, Van Houcke J, Almeida DF, Paelinck L, Peiffer M, Steenackers G, Vandermeulen D. Cascaded statistical shape model based segmentation of the full lower limb in CT.. Comput Methods Biomech Biomed Engin 2019 May;22(6):644-657.
    doi: 10.1080/10255842.2019.1577828pubmed: 30822149google scholar: lookup
  15. Kainmueller D, Lamecker H, Zachow S, Hege HC. An articulated statistical shape model for accurate hip joint segmentation.. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:6345-51.
    doi: 10.1109/IEMBS.2009.5333269pubmed: 19964159google scholar: lookup
  16. Zheng G, Dong X, Rajamani KT, Zhang X, Styner M, Thoranaghatte RU, Nolte LP, Ballester MA. Accurate and robust reconstruction of a surface model of the proximal femur from sparse-point data and a dense-point distribution model for surgical navigation.. IEEE Trans Biomed Eng 2007 Dec;54(12):2109-22.
    doi: 10.1109/TBME.2007.895736pubmed: 18075027google scholar: lookup
  17. Balestra S, Schumann S, Heverhagen J, Nolte L, Zheng G. Articulated statistical shape model-based 2D-3D reconstruction of a hip joint.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8498. Fukuoka: (2014).
    doi: 10.1007/978-3-319-07521-1_14google scholar: lookup
  18. Zheng G, Yu W. Statistical shape and deformation models based 2D-3D reconstruction.. Statistical Shape and Deformation Analysis Amsterdam: Elsevier; (2017). p. 329–49.
    doi: 10.1016/B978-0-12-810493-4.00015-8google scholar: lookup
  19. Campbell JQ, Petrella AJ. Automated finite element modeling of the lumbar spine: Using a statistical shape model to generate a virtual population of models.. J Biomech 2016 Sep 6;49(13):2593-2599.
    doi: 10.1016/j.jbiomech.2016.05.013pubmed: 27270207google scholar: lookup
  20. Väänänen SP, Grassi L, Flivik G, Jurvelin JS, Isaksson H. Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image.. Med Image Anal 2015 Aug;24(1):125-134.
    doi: 10.1016/j.media.2015.06.001pubmed: 26148575google scholar: lookup
  21. Cerveri P, Belfatto A, Baroni G, Manzotti A. Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia.. Int J Med Robot 2018 Dec;14(6):e1947.
    doi: 10.1002/rcs.1947pubmed: 30073759google scholar: lookup
  22. Fitzpatrick CK, Baldwin MA, Laz PJ, FitzPatrick DP, Lerner AL, Rullkoetter PJ. Development of a statistical shape model of the patellofemoral joint for investigating relationships between shape and function.. J Biomech 2011 Sep 2;44(13):2446-52.
    doi: 10.1016/j.jbiomech.2011.06.025pubmed: 21803359google scholar: lookup
  23. Hespel AM, Wilhite R, Hudson J. Invited review--Applications for 3D printers in veterinary medicine.. Vet Radiol Ultrasound 2014 Jul-Aug;55(4):347-58.
    doi: 10.1111/vru.12176pubmed: 24889058google scholar: lookup
  24. Brinkschulte M, Bienert-Zeit A, Lüpke M, Hellige M, Staszyk C, Ohnesorge B. Using semi-automated segmentation of computed tomography datasets for three-dimensional visualization and volume measurements of equine paranasal sinuses.. Vet Radiol Ultrasound 2013 Nov-Dec;54(6):582-90.
    doi: 10.1111/vru.12080pubmed: 23890087google scholar: lookup
  25. Lima LFSd, Barros AJBPd, Martini AdC, Stocco MB, Kuczmarski AH, Souza RLd. Photogrammetry and 3D prototyping: a low-cost resource for training in veterinary orthopedics.. Ciência Rural (2019) 49.
    doi: 10.1590/0103-8478cr20180929google scholar: lookup
  26. Kozic N, Weber S, Büchler P, Lutz C, Reimers N, González Ballester MA, Reyes M. Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets.. Med Image Anal 2010 Jun;14(3):265-75.
    doi: 10.1016/j.media.2010.02.008pubmed: 20359938google scholar: lookup
  27. Tkany L, Hofstätter B, Petersik A, Miehling J, Wartzack S, Sesselmann S. New Design Process for Anatomically Enhanced Osteosynthesis Plates.. J Orthop Res 2019 Jul;37(7):1508-1517.
    doi: 10.1002/jor.24299pubmed: 30977547google scholar: lookup
  28. Liley H, Zhang J, Firth EC, Fernandez JW, Besier TF. Statistical modeling of the equine third metacarpal bone incorporating morphology and bone mineral density.. PLoS One 2018;13(6):e0194406.
    doi: 10.1371/journal.pone.0194406pmc: PMC5991359pubmed: 29874224google scholar: lookup
  29. Wilson A, Agass R, Vaux S, Sherlock E, Day P, Pfau T, Weller R. Foot placement of the equine forelimb: Relationship between foot conformation, foot placement and movement asymmetry.. Equine Vet J 2016 Jan;48(1):90-6.
    doi: 10.1111/evj.12378pubmed: 25523459google scholar: lookup
  30. Chateau H, Degueurce C, Jerbi H, Crevier-Denoix N, Pourcelot P, Audigié F, Pasqui-Boutard V, Denoix JM. Normal three-dimensional behaviour of the metacarpophalangeal joint and the effect of uneven foot bearing.. Equine Vet J Suppl 2001 Apr;(33):84-8.
    doi: 10.1111/j.2042-3306.2001.tb05366.xpubmed: 11721577google scholar: lookup
  31. Joostens Z, Evrard L, Busoni V. Unipodal stance influences radiographic evaluation of foot balance in horses.. Vet Radiol Ultrasound 2019 May;60(3):273-279.
    doi: 10.1111/vru.12729pubmed: 30864267google scholar: lookup
  32. Krčah M, Székely G, Blanc R. Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior.. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Chicago, IL: (2011). p. 2087–90.
  33. Grothausmann R, Beare R, Gout J, Kühnel M. Providing values of adjacent voxel with vtkDiscreteMarchingCubes.. VTK J (2016) 975.
  34. Valette S, Chassery JM, Prost R. Generic remeshing of 3D triangular meshes with metric-dependent discrete voronoi diagrams.. IEEE Trans Vis Comput Graph 2008 Mar-Apr;14(2):369-81.
    doi: 10.1109/TVCG.2007.70430pubmed: 18192716google scholar: lookup
  35. Attene M. A lightweight approach to repairing digitized polygon meshes.. Vis Comput (2010) 26:1393–406.
    doi: 10.1007/s00371-010-0416-3google scholar: lookup
  36. McGuigan MP, Wilson AM. The effect of gait and digital flexor muscle activation on limb compliance in the forelimb of the horse Equus caballus.. J Exp Biol 2003 Apr;206(Pt 8):1325-36.
    doi: 10.1242/jeb.00254pubmed: 12624168google scholar: lookup
  37. Panagiotopoulou O, Rankin JW, Gatesy SM, Hutchinson JR. A preliminary case study of the effect of shoe-wearing on the biomechanics of a horse's foot.. PeerJ 2016;4:e2164.
    doi: 10.7717/peerj.2164pmc: PMC4950542pubmed: 27478694google scholar: lookup
  38. Merritt JS, Davies HM, Burvill C, Pandy MG. Influence of muscle-tendon wrapping on calculations of joint reaction forces in the equine distal forelimb.. J Biomed Biotechnol 2008;2008:165730.
    doi: 10.1155/2008/165730pmc: PMC2396215pubmed: 18509485google scholar: lookup
  39. Danckaers F, Huysmans T, Lacko D, Ledda A, Verwulgen S, Van Dongen S. Correspondence preserving elastic surface registration with shape model prior.. 2014 22nd International Conference on Pattern Recognition Stockholm: (2014). p. 2143–8.
    doi: 10.1109/ICPR.2014.373google scholar: lookup
  40. Fletcher PT, Lu C, Pizer SM, Joshi S. Principal geodesic analysis for the study of nonlinear statistics of shape.. IEEE Trans Med Imaging 2004 Aug;23(8):995-1005.
    doi: 10.1109/TMI.2004.831793pubmed: 15338733google scholar: lookup
  41. Davies RH, Twining CJ, Cootes TF, Waterton JC, Taylor CJ. A minimum description length approach to statistical shape modeling.. IEEE Trans Med Imaging 2002 May;21(5):525-37.
    doi: 10.1109/TMI.2002.1009388pubmed: 12071623google scholar: lookup
  42. Su Z. Statistical Shape Modelling: Automatic Shape Model Building.. London, UK: University College London; (2011).
  43. McClinchey H, Thomason J, Jofriet J. Isolating the effects of equine hoof shape measurements on capsule strain with finite element analysis.. Vet Compar Orthop Traumatol (2003) 16:67–75.
    doi: 10.1055/s-0038-1632762google scholar: lookup
  44. Zsoldos RR, Groesel M, Kotschwar A, Kotschwar AB, Licka T, Peham C. A preliminary modelling study on the equine cervical spine with inverse kinematics at walk.. Equine Vet J Suppl 2010 Nov;(38):516-22.
    doi: 10.1111/j.2042-3306.2010.00265.xpubmed: 21059054google scholar: lookup

Citations

This article has been cited 2 times.
  1. Crecan CM, Peștean CP. Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review. Sensors (Basel) 2023 Jul 11;23(14).
    doi: 10.3390/s23146301pubmed: 37514599google scholar: lookup
  2. He H, Banks SA, Biedrzycki AH. Anatomical variations of the equine femur and tibia using statistical shape modeling. PLoS One 2023;18(6):e0287381.
    doi: 10.1371/journal.pone.0287381pubmed: 37390069google scholar: lookup
Subscribe to our newsletter
Join 99,005 horse owners like you who receive our email updates on horse health and nutrition.