Abstract
Shape alterations in body organs are common pathological hallmarks of multiple disorders, making quantitative shape analysis key for obtaining diagnostic and prognostic biomarkers. In this context, Geometric Morphometrics (GM) is a powerful approach to capture subtle yet significant dysmorphologies. Since GM relies on registering landmarks on 3D anatomical structures, developing generic, automatic and accurate 3D landmarking methods is key for building high-throughput morphometric tools. This study compares state-of-the-art deep learning and template-based 3D landmarking methods using MRI datasets of faces, upper airways, and hippocampi. We evaluated these methods in terms of landmarking error and morphometric variables relative to manual annotations. Our results show that architecture-reused deep learning methods are more accurate and faster in inference than template-based techniques, particularly for anatomical structures with high shape variability, even with fewer training examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis: With Applications in R, vol. 995. Wiley, Chichester (2016)
Hallgrímsson, B., et al.: Automated syndrome diagnosis by three-dimensional facial imaging. Genet. Med. 22(1010), 1682–1693 (2020). https://doi.org/10.1038/s41436-020-0845-y
El-Baz, A., Nitzken, M., Elnakib, A., et al.: 3D shape analysis for early diagnosis of malignant lung nodules. Med. Image Comput. Comput. Assist. Interv. 14(Pt 3), 175–182 (2011)
Elnakib, A., Casanova, M.F., Gimel’farb, G., El-Baz, A.: Autism diagnostics by 3D shape analysis of the corpus callosum. In: Suzuki, K. (ed.) Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, pp. 315–335. IGI Global (2012)
Monna, F., Ben Messaoud, R., Navarro, N., et al.: Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans. Sleep Med. 95, 76–83 (2022)
Mitteroecker, P., Schaefer, K.: Thirty years of geometric morphometrics: achievements, challenges, and the ongoing quest for biological meaningfulness. Am. J. Biol. Anthropol. 178, 181–210 (2022)
Huanca Ghislanzoni, L., Lione, R., Cozza, P., Franchi, L.: Measuring 3D shape in orthodontics through geometric morphometrics. Prog. Orthod. 18(1), 38 (2017)
Lois Zlolniski, S., Torres-Tamayo, N., García-Martínez, D., et al.: 3D geometric morphometric analysis of variation in the human lumbar spine. Am. J. Phys. Anthropol. 170(3), 361–372 (2019)
Laganà, G., Venza, N., Paoloni, V., Bertoldo, F., Ruvolo, G., Cozza, P.: A 3D geometric morphometric analysis of the palatal morphology in Marfan’s syndrome: a preliminary study. J. Clin. Diagn. Res. 12(1), ZC14–ZC17 (2018)
Zhong, Y., Chen, Z., Li, B., Ma, H., Yang, B.: Correlation analysis of airway-facial phenotype in Crouzon syndrome by geometric morphometrics: a promising method for non-radiation airway evaluation. Orthod. Craniofac. Res. 27, 504–513 (2024)
Starbuck, J.M., et al.: Green tea extracts containing epigallocatechin-3-gallate modulate facial development in Down syndrome. Sci. Rep. 11(1), 4715 (2021)
Percival, C.J., Devine, J., Darwin, B.C., et al.: The effect of automated landmark identification on morphometric analyses. J. Anat. 234(6), 917–935 (2019). https://doi.org/10.1111/joa.13059. Published correction appears in J Anat. 2019 235(5):1018
Li, M., et al.: Rapid automated landmarking for morphometric analysis of three-dimensional facial scans. J. Anat. 230(4), 607–618 (2017)
Liang, S., Wu, J., Weinberg, S.M., Shapiro, L.G.: Improved detection of landmarks on 3D human face data. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6482–6485 (2013)
Papazov, C., Marks, T., Jones, M.: Real-time head pose and facial landmark estimation from depth images using triangular surface patch features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4722–4730 (2015)
Abu, A., Ngo, C.G., Abu-Hassan, N., Othman, S.A.: Automated craniofacial landmarks detection on 3D image using geometry characteristics information. BMC Bioinform. 19(Suppl 13), 548 (2019)
de Jong, M.A., Hysi, P., Spector, T., et al.: Ensemble landmarking of 3D facial surface scans. Sci. Rep. 8, 12 (2018)
Paulsen, R.R., Juhl, K.A., Haspang, T.M., Hansen, T., Ganz, M., Einarsson, G.: Multi-view consensus CNN for 3D facial landmark placement. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 706–719. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20887-5_44
Zhang, Y., et al.: An automated method of 3D facial soft tissue landmark prediction based on object detection and deep learning. Diagnostics 13(11), 1853 (2023)
He, Z., et al.: FST-NET: facial soft tissue landmark localization on 3DMD scans using feature fusion and local coordinate regression. In: Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (2024)
Berends, B., Bielevelt, F., Schreurs, R., Vinayahalingam, S., Maal, T., de Jong, G.: Fully automated landmarking and facial segmentation on 3D photographs. Sci. Rep. 14(1), 6463 (2024)
Devine, J., Aponte, J.D., Katz, D.C., et al.: A registration and deep learning approach to automated landmark detection for geometric morphometrics. Evol. Biol. 47, 246–259 (2020). https://doi.org/10.1007/s11692-020-09508-8
Porto, A., Rolfe, S., Maga, A.M.: ALPACA: a fast and accurate computer vision approach for automated landmarking of three-dimensional biological structures. Methods Ecol. Evol. 12(11), 2129–2144 (2021)
Rohlf, F.J., Slice, D.: Extensions of the procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990)
Zhang, C., Porto, A., Rolfe, S., Kocatulum, A., Maga, A.M.: Automated landmarking via multiple templates. PLoS ONE 17(12), e0278035 (2022)
Wen, A., Zhu, Y., Xiao, N., et al.: Comparison study of extraction accuracy of 3D facial anatomical landmarks based on non-rigid registration of face template. Diagnostics (Basel) 13(6), 1086 (2023). Published 2023 Mar 13
Serafin, M., Baldini, B., Cabitza, F., et al.: Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis. Radiol. Med. 128(5), 544–555 (2023)
Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3), 907–922 (2011)
Stull, K.E., Tise, M.L., Ali, Z., Fowler, D.R.: Accuracy and reliability of measurements obtained from computed tomography 3D volume rendered images. Forensic Sci. Int. 238, 133–140 (2014)
Acknowledgements
The research in this paper was supported by the Joan Oró grant (2024 FI-3 00160) from the Recerca i Universitats Departament (DRU) of the Generalitat de Catalunya with grant 2023 FI-2 00160 and the European Social Fund, by Agencia Española de Investigación (PID2020-113609RB-C21/AEI/10.13039/501100011033), by Instituto de Salud Carlos III (ISCIII) through the contracts FI21/00093 and CP20/00072 (co-funded by European Regional Development Fund (ERDF)/European Social Fund “Investing in your future”) and by Fondation Jerome Lejeune with grant 2020b cycle-Project No.2001. The authors would also like to thank the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) of the Generalitat de Catalunya (2021 SGR01396, 2021 SGR00706, 2021 SGR1475).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Heredia-Lidón, Á. et al. (2025). A Critical Comparison Between Template-Based and Architecture-Reused Deep Learning Methods for Generic 3D Landmarking of Anatomical Structures. In: Wachinger, C., Paniagua, B., Elhabian, S., Luijten, G., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2024. Lecture Notes in Computer Science, vol 15275. Springer, Cham. https://doi.org/10.1007/978-3-031-75291-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-75291-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-75290-2
Online ISBN: 978-3-031-75291-9
eBook Packages: Computer ScienceComputer Science (R0)
