Acta Univ. Agric. Silvic. Mendelianae Brun. 2020, 68(1), 63-72 | DOI: 10.11118/actaun202068010063

Automated Estimation of Loin Muscle Mass in Living Rabbits Using Computed Tomography

Zsolt Matics1, György Kovács2, Ádám Csóka3, Virág Ács1, Rozália Kasza1, Örs Petneházy3, István Nagy1, Rita Garamvölgyi3, Zsolt Petrási3, Tamás Donkó3
1 Kaposvár University, Faculty of Agricultural and Environmental Sciences, Guba S. str. 40., H-7400 Kaposvár, Hungary
2 Analytical Minds Ltd., Cívis str. 8. 1/9., H-4032 Debrecen, Hungary
3 Medicopus Nonprofit Ltd., Research Department, Guba S. str. 40., H-7400 Kaposvár, Hungary

The objective of this study was to present the updated segmentation technique predicting the loin muscle weight of rabbits based on in vivo computed tomography measurements.
The segmented muscle volumes are used to estimate the weight of the loin muscle and the predicted weights are compared to the real weights of meat cuts measured after the dissection of the animals. The R2 value of the proposed technique is 0.74 which is significantly better than the determination coefficients than that of the of the previously used method (R2 0.49). The proposed technique is suitable to be involved in the breeding selection program of rabbits (Pannon White) at Kaposvár University, Hungary.

Keywords: computed tomography, automated segmentation, loin muscle, rabbit
Grants and funding:

This work was supported by the Emberi Erőforrások Minisztériuma (EFOP-3.6.3.-Vekop-16-2017-00005) and the János Bolyai Research Scholarship BO/00871/19 of the Hungarian Academy of Sciences. The publication was supported by the ÚNKP-19-4-KE-24 New National Excellence Program of the Ministry for Innovation and Technology.

Received: July 4, 2019; Accepted: January 8, 2020; Published: February 27, 2020  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Matics, Z., Kovács, G., Csóka, Á., Ács, V., Kasza, R., Petneházy, Ö., ... Donkó, T. (2020). Automated Estimation of Loin Muscle Mass in Living Rabbits Using Computed Tomography. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 68(1), 63-72. doi: 10.11118/actaun202068010063
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. CARABÚS, A., GISPERT, M., BRUN, A., RODRÍGUEZ, P. and FONT-I-FURNOLS, M. 2014. In vivo computed tomography evaluation of the composition of the carcass and main cuts of growing pigs of three commercial crossbreeds. Livestock Science, 170: 181-192. DOI: 10.1016/j.livsci.2014.10.005 Go to original source...
    2. FEDOROV, A., BEICHEL, R., KALPATHY-CRAMER, J., FINET, J., FILLION-ROBIN, J.-C., PUJOL, S., BAUER, C., JENNINGS, D., FENNESSY, F., SONKA, M., BUATTI, J., AYLWARD, S., MILLER, J. V., PIEPER, S. and KIKINIS, R. 2012. 3D Slicer as an image computing platform for the quantitative imaging network. Magnetic Resonance Imaging, 30(9): 1323-1341. DOI: 10.1016/j.mri.2012.05.001 Go to original source...
    3. FONT-I-FURNOLS, M., BRUN, A., MARTI, S., REALINI, C. E., PÉREZ-JUAN, M., GONZALEZ, J. and DEVANT, M. 2014. Composition and intramuscular fat estimation of Holstein bull and steer rib sections by using one or more computed tomography cross-sectional images. Livestock Science, 170: 210-218. DOI: 10.1016/j.livsci.2014.10.009 Go to original source...
    4. FONT-I-FURNOLS, M., BRUN, A., TOUS, N. and GISPERT, M. 2013. Use of linear regression and partial least square regression to predict intramuscular fat of pig loin computed tomography images. Chemometrics and Intelligent Laboratory Systems, 122: 58-64. DOI: 10.1016/j.chemolab.2013.01.005 Go to original source...
    5. FONT-I-FURNOLS, M., CARABÚS, A., POMAR, C. and GISPERT, M. 2015. Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes. Animal, 9(1): 166-178. DOI: 10.1017/S1751731114002237 Go to original source...
    6. FONT-I-FURNOLS, M., TERAN, M. F. and GISPERT, M. 2009. Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression. Chemometrics and Intelligent Laboratory Systems, 98(1): 31-37. DOI: 10.1016/j.chemolab.2009.04.009 Go to original source...
    7. GANGSEI, L. E., KONGSRO, J., OLSEN, E. V., RØE, M., ALVSEIKE, O. and SAEBØ, S. 2016. Prediction precision for lean meat percentage in Norwegian pig carcasses using 'Hennessy grading probe 7': Evaluation of methods emphasized at exploiting additional information from computed tomography. Acta Agriculturae Scandinavica A: Animal Sciences, 66: 17-24. Go to original source...
    8. GANGSEI, L. E., KONGSRO, J., OLSTAD, K., GRINDFLEK, E. and SAEBØ, S. 2016. Building an in vivo anatomical atlas to close the phenomic gap in animal breeding. Computers and Electronics in Agriculture, 127: 739-743. DOI: 10.1016/j.compag.2016.08.003 Go to original source...
    9. GJERLAUG-ENGER, E., KONGSRO, J., ØDEGÅRD, J., AASS, L. and VANGEN, O. 2012. Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc. Animal, 6(1): 9-18. DOI: 10.1017/S1751731111001455 Go to original source...
    10. GLASBEY, C. A. and ROBINSON, C. D. 2002. Estimators of tissue proportions from X-ray CT images. Biometrics, 58(4): 928-936. DOI: 10.1111/j.0006-341X.2002.00928.x Go to original source...
    11. HO, H., YU, H. B., GANGSEI, L. E. and KONGSRO, J. 2019. A CT-image based pig atlas model and its potential applications in the meat industry. Meat Science, 148: 1-4. DOI: 10.1016/j.meatsci.2018.09.011 Go to original source...
    12. JONES, H. E., LEWIS, R. M., YOUNG, M. J. and WOLF, B. T. 2002. The use of X-ray computer tomography for measuring the muscularity of live sheep. Animal Science, 75(03): 387-399. DOI: 10.1017/S1357729800053169 Go to original source...
    13. LAMBE, N. R., WOOD, J. D., MCLEAN, K. A., WALLING, G. A., WHITNEY, H., JAGGER, S., FULLARTON, P., BAYNTUN, J. and BÜNGER, L. 2013. Effects of low protein diets on pigs with a lean genotype 2. Compositional traits measured with computed tomography (CT). Meat Science, 95(1): 129-136. DOI: 10.1016/j.meatsci.2013.04.038 Go to original source...
    14. LAMBE, N. R., MCLEAN, K. A., GORDON, J., EVANS, D., CLELLAND, N. and BUNGER, L. 2017. Prediction of intramuscular fat content using CT scanning of packaged lamb cuts and relationships with meat eating quality. Meat Science, 123: 112-119. DOI: 10.1016/j.meatsci.2016.09.008 Go to original source...
    15. LARSEN, R. 2000. 3-D contextual Bayesian classifiers. IEEE Transactions on Image Processing 10(3): 518-524. DOI: 10.1109/83.826789 Go to original source...
    16. MATICS, Z., NAGY, I., GERENCSER, Z., RADNAI, I., GYOVAI, P., DONKO, T., DALLE ZOTTE, A., CURIK, I. and SZENDRO, Z. 2014. Pannon Breeding Program in rabbit at Kaposvar University. World Rabbit Science, 22(4): 287-300. DOI: 10.4995/wrs.2014.1511 Go to original source...
    17. NAGY, I., IBÁÑEZ, N., ROMVÁRI, R., MEKKAWY, W., METZGER, S., HORN, P. and SZENDRO, Z. 2006. Genetic parameters of growth and in vivo computerized tomography based carcass traits in Pannon White rabbits. Livestock Science, 104(1-2): 46-52. DOI: 10.1016/j.livsci.2006.03.009 Go to original source...
    18. NAVAJAS, E. A., LAMBE, N. R., MCLEAN, K. A., GLASBEY, C. A., FISHER, A. V., CHARTERIS, A. J. L., BÜNGER, L. and SIMM, G. 2007. Accuracy of in vivo muscularity indices measured by computed tomography and their association with carcass quality in lambs. Meat Science, 75(3): 533-542. DOI: 10.1016/j.meatsci.2006.09.007 Go to original source...
    19. NGO, L., HO, H., HUNTER, P., QUINN, K., THOMSON, A. and PEARSON, G. 2016. Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics. Meat Science, 112: 39-45. DOI: 10.1016/j.meatsci.2015.10.012 Go to original source...
    20. R CORE TEAM. 2013. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
    21. ROMVÁRI, R., MILISITS, G., SZENDRÖ, Z. and SORENSEN, P. 1996. Non invasive method to study the body composition of rabbits by X-ray computerised tomography. World Rabbit Science, 4: 219-224. Go to original source...
    22. SCHOLZ, A. M., BÜNGER, L., KONGSRO, J., BAULAIN, U. and MITCHELL, A. D. 2015. Non-invasive methods for the determination of body and carcass composition in livestock: Dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: Invited review. Animal, 9(7): 1250-1264. DOI: 10.1017/S1751731115000336 Go to original source...
    23. SVIHUS, B. and KATIE, J. 1993. Computerized Tomography as a Tool to Predict Composition Traits in Broilers. Comparisons of Results across Samples and Years, Acta Agriculturae Scandinavica, Section A - Animal Science, 43(4): 214-218. DOI: 10.1080/09064709309410169 Go to original source...
    24. SZENDRO, Z., METZGER, S., NAGY, I., SZABÓ, A., PETRÁSI, Z., DONKÓ, T. and HORN, P. 2012. Effect of divergent selection for the computer tomography measured thigh muscle volume on productive and carcass traits of growing rabbits. Livestock Science, 149(1-2): 167-172. DOI: 10.1016/j.livsci.2012.07.011 Go to original source...
    25. VESTER-CHRISTENSEN, M., ERBOU, S. G. H., HANSEN, M. F., OLSEN, E. V., CHRISTENSEN, L. B., HVIID, M., ERSBØLL, B. K. and LARSEN, R. 2009. Virtual dissection of pig carcasses. Meat Science, 81(4): 699-704. DOI: 10.1016/j.meatsci.2008.11.015 Go to original source...
    26. XIBERTA, P., BOADA, I., BARDERA, A. and FONT-I-FURNOLS, M. 2017. A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images. Computers and Electronics in Agriculture, 140: 290-302. DOI: 10.1016/j.compag.2017.06.003 Go to original source...
    27. ZHOU, X., HAYASHI, T., HARA, T., FUJITA, H., YOKOYAMA, R., KIRYU, T. and HOSHI, H. 2006. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images. Computerized Medical Imaging and Graphics, 30(5): 299-313. DOI: 10.1016/j.compmedimag.2006.06.002 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content