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Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
Published on: March 19, 2017
3D geometric morphometrics in veterinary science: applications, standardization, and future directions
T Szara1, N Hadžiomerović2, C Bakıcı3
1Department of Morphological Sciences, Institute of Veterinary Medicine, Warsaw University of Life Sciences-SGGW, 02-776 Warsaw, Poland.
Abstract:
Three-dimensional geometric morphometric methods have emerged as a pivotal tool in veterinary anatomy, taxonomy, clinical research, and studies of morphological diversity. This article summarizes the key stages, applications, clinical potential, and recommendations for data standardization in 3D morphometrics. Datasets are typically acquired using radiological modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and 3D surface scanning, each offering specific advantages and constraints contingent on the research context. Standardized landmark sets are essential in 3D morphometric studies to ensure reproducibility and comparability of results across independent investigations. Consistent use of reference landmarks enables repeatable analyses, but the number of landmarks directly influences the required sample size and statistical power. Consequently, a minimal yet balanced landmark configuration is critical. This article proposes a standardized, minimal landmark set for the skulls of horses, cattle, and sheep to enhance inter-study reproducibility and comparability. Landmark selection prioritizes anatomically distinct points to avoid excessive landmarking, which may complicate analyses or compromise interpretability. Applications of 3D morphometric methods include orthopedic surgical planning, biomechanical modeling, and assessment of congenital anomalies, providing enhanced precision in diagnostics and research. In conclusion, 3D geometric morphometric methods represent a robust analytical framework in veterinary anatomy, morphology, and clinical research. Their significance is poised to grow through integration with automated landmarking, artificial intelligence-driven analyses, and international data-sharing networks, thereby advancing scientific inquiry in novel dimensions.

