Digital Morphology: The Final Frontier

Year 2022, Issue: 2, 45 - 59, 29.12.2022

A. Murat Maga

https://doi.org/10.26650/IAR2022-1174374

Abstract

Morphology is central to biological anthropology and its allied fields of anatomical sciences, forensics, and other related disciplines. Many biological anthropology students have their first real foray into the discipline after completing a course in osteology, craniometry, or vertebrate morphology. Unfortunately, the natural history collections that support this type of research and training have not grown. Many countries have strict rules about natural history specimen collections, and these collections seem to be concentrated in a few developed countries, regardless of where the specimens had been collected. Thus, access to comparative material can be problematic where such collections are not readily available. Even if collections are available, accessing them can be severely restricted due to external circumstances, as the prolonged pandemic has shown. Luckily, digital morphology has emerged over the last decade as a new field that stands to change the landscape of specimenbased research and training. Concerted 2D and 3D digitization efforts, the emergence of online aggregate specimen repositories, and availability of comprehensive open-source software tools (such as 3D Slicer) for utilizing these resources has conveniently transformed the field of quantitative and comparative morphology. In this brief review, I will focus explicitly on the 3D Slicer ecosystem and how it can be leveraged as part of a curriculum or research program on digital morphology. In a nutshell, the primary differentiator of the 3D Slicer is not that it is just free but that it is open-source and extensible, making access to digital data more equitable for everyone. I will particularly focus on the 3D Slicer’s SlicerMorph extension, which facilitates 3D geometric morphometric data collection and analysis within the Slicer ecosystem, so all the steps in the digital morphology workflow from import, visualization, and data collection to visualizing the morpho-space can be achieved in a single, well-documented environment.

Keywords

Digital morphology, Quantitative morphology, Geometric morphometrics, 3D imaging, Segmentation, Visualization

Supporting Institution

National Science Foundation

Project Number

BIO 1759883

Thanks

I thank more than 200 participants of our short courses and workshops for their valuable feedback which keep improving SlicerMorph.

Digital Morphology: Final Frontier

Abstract

Morphology is central to biological anthropology and its allied fields of anatomical sciences, forensics and other related disciplines. Many biological anthropology students have their first real foray into the discipline after completing a course in osteology, craniometry or vertebrate morphology. Unfortunately, the natural history collections that support this type of research and training haven't been growing. Many countries have strict rules about natural history specimen collections, and these collections seem to be concentrated in a few developed countries, regardless of where the specimens are collected from. Thus, access to comparative material can be problematic, where such collections are not readily available. Even in the case of availability of such collections, as the prolonged pandemic showed us, access to them can be severely restricted due circumstances. Luckily, a new field, Digital Morphology, has been emerging in the last decade, changing the landscape of specimen-based research and training. The concerted 2D and 3D digitization efforts, emergence of aggregate specimen repositories, and availability of comprehensive open-source software tools (such as 3D Slicer) to utilize these resources conveniently is causing a transformation in study of quantitative and comparative morphology. In this brief review, I will focus explicitly on the 3D Slicer ecosystem and how it can be leveraged as part of a curriculum or a research program in digital morphology. In a nutshell, the primary differentiator of the 3D Slicer is not that it is just free, but it is open-source and extensible, making access to such digital data more equitable for everyone. I will particularly focus on SlicerMorph extension of the 3D Slicer, which facilitates 3D geometric morphometric data collection and analysis within the Slicer ecosystem, so all the step of digital morphology workflow from import to visualization to data collection to visualization of morphospace can be achieved in a single, well-documented environment.

Keywords

digital morphology, quantitative morphology, geometric morphometrics, 3D imaging, segmentation, visualization, Digital morphology, Quantitative morphology, Geometric morphometrics, 3D imaging, Segmentation, Visualization

Project Number

BIO 1759883

References

• Adams, D. C., & Otarola-Castillo, E. (2013). geomorph: An R package for the collection and analysis of geometric morphometric shape data. Methods in Ecology and Evolution, 4, 393-399. google scholar
• Adams, Dean C. (2016). Evaluating modularity in morphometric data: Challenges with the RV coefficient and a new test measure. Methods in Ecology and Evolution, n/a-n/a. https://doi.org/10.1111/2041-210X.12511 google scholar
• Adams, Dean C., Rohlf, F. J., & Slice, D. E. (2013). A field comes of age: Geometric morphometrics in the 21st century. Hystrix-Italian Journal of Mammalogy, 24(1), 7-14. https://doi.org/10.4404/ hystrix-24.1-6283 google scholar
• Berry, S. D., & Edgar, H. J. (2021). Announcement: The New Mexico decedent image database. Forensic Imaging, 24, 200436. https://doi.org/10.1016/j.fri.2021.200436 google scholar
• Bookstein, F. L. (1997). Landmark methods for forms without landmarks: Morphometrics of group differences in outline shape. Medical Image Analysis, 1(3), 225-243. https://doi.org/10.1016/S1361-8415(97)85012-8 google scholar
• Boyer, D. M., Gunnell, G. F., Kaufman, S., & McGeary, T. M. (2016). MORPHOSOURCE: ARCHIVING AND SHARING 3-D DIGITAL SPECIMEN DATA. The Paleontological Society Papers, 22, 157181. https://doi.org/10.1017/scs.2017.13 google scholar
• Diaz-Pinto, A., Alle, S., Ihsani, A., Asad, M., Nath, V., Perez-Garcia, F., Cardoso, M. J. (2022). MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images. ArXiv E- Prints. Retrieved from https://arxiv.org/pdf/2203.12362.pdf google scholar
• Dryden, I. L., & Mardia, K. M. (2008). Statistical Shape Analysis. Chicester: John Wiley and Sons. google scholar
• Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., ... Kikinis, R. (2012). 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic Resonance Imaging, 30(9), 1323-1341. https://doi.org/10.1016/j.mri.2012.05.001 google scholar
• Goodall, C. (1991). Procrustes Methods in the Statistical Analysis of Shape. Journal of the Royal Statistical Society. Series B (Methodological), 53(2), 285-339. google scholar
• Kapur, T., Pieper, S., Fedorov, A., Fillion-Robin, J.-C., Halle, M., O’Donnell, L., . Kikinis, R. (2016). Increasing the impact of medical image computing using community-based open- access hackathons: The NA-MIC and 3D Slicer experience. Medical Image Analysis, 33, 176-180. https://doi. org/10.1016/j.media.2016.06.035 google scholar
• Kent, J. T., & Mardia, K. V. (1997). Consistency of Procrustes Estimators. Journal of the Royal Statistical Society. Series B (Methodological), 59(1), 281-290. google scholar
• Kikinis, R., Pieper, S. D., & Vosburgh, K. G. (2014). 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. In Intraoperative Imaging and Image- Guided Therapy (pp. 277-289). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7657-3_19 google scholar
• Klingenberg, C. P. (2010). Evolution and development of shape: Integrating quantitative approaches. Nature Reviews Genetics, 11(9), 623-635. https://doi.org/10.1038/nrg2829 google scholar
• Klingenberg, C. P. (2011). MorphoJ: An integrated software package for geometric morphometrics. Molecular Ecology Resources, 11(2), 353-357. https://doi.org/10.1111/j.1755- 0998.2010.02924.x google scholar
• Klingenberg, C. P. (2015). Analyzing Fluctuating Asymmetry with Geometric Morphometrics: Concepts, Methods, and Applications. Symmetry, 7(2), 843-934. https://doi.org/10.3390/sym7020843 google scholar
• Klingenberg, C. P. (2016). Size, shape, and form: Concepts of allometry in geometric morphometrics. Development Genes and Evolution, 226, 113-137. https://doi.org/10.1007/s00427- 016-0539-2 google scholar
• Mitteroecker, P., & Gunz, P. (2009). Advances in Geometric Morphometrics. Evolutionary Biology, 36(2), 235-247. https://doi.org/10.1007/s11692-009-9055-x google scholar
• Porto, A., Rolfe, S., & Maga, A. M. (2021). ALPACA: A fast and accurate computer vision approach for automated landmarking of three-dimensional biological structures. Methods in Ecology and Evolution, 12(11), 2129-2144. https://doi.org/10.1111/2041-210X.13689 google scholar
• Punzo, D., van der Hulst, J. M., Roerdink, J. B. T. M., Fillion-Robin, J. C., & Yu, L. (2017). SlicerAstro: A 3-D interactive visual analytics tool for HI data. Astronomy and Computing, 19, 45-59. https://doi. org/10.1016/j.ascom.2017.03.004 google scholar
• Richtsmeier, J. T., Deleon, V. B., & Lele, S. R. (2002). The promise of geometric morphometrics. Yearbook of Physical Anthropology, Vol 45, 45, 63-91. https://doi.org/10.1002/ajpa.10174 google scholar
• Rohlf, F. J. (2000). Statistical power comparisons among alternative morphometric methods. American Journal of Physical Anthropology, 111(4), 463-478. https://doi.org/10.1002/(SICI)1096-8644(200004)111:4<463::AID-AJPA3>3.0.CO;2-B google scholar
• Rohlf, F. J., & Slice, D. (1990). Extensions of the Procrustes Method for the Optimal Superimposition of Landmarks. Systematic Zoology, 39(1), 40-59. https://doi.org/10.2307/2992207 google scholar
• Rolfe, S., Davis, C., & Maga, A. M. (2021). Comparing semi-landmarking approaches for analyzing three-dimensional cranial morphology. American Journal of Physical Anthropology, 175(1), 227-237. https://doi.org/10.1002/ajpa.24214 google scholar
• Rolfe, S. M., & Maga, A. M. (2022, August 29). Deep Learning Enabled Multi-Organ Segmentation of Mouse Embryos (p. 2022.08.26.505447). p. 2022.08.26.505447. bioRxiv. https://doi. org/10.1101/2022.08.26.505447 google scholar
• Rolfe, S., Pieper, S., Porto, A., Diamond, K., Winchester, J., Shan, S., ... Maga, A. M. (2021). SlicerMorph: An open and extensible platform to retrieve, visualize and analyse 3D morphology. Methods in Ecology and Evolution, 12(10), 1816-1825. https://doi.org/10.1111/2041-210X.13669 google scholar
• Zelditch, M. (2012). Geometric morphometrics for biologists: A primer. Amsterdam: Elsevier/Academic Press. google scholar
• Zhang, C., Porto, A., Rolfe, S., Kocatulum, A., & Maga, A. M. (2022, January 25). Automated Landmarking via Multiple Templates (p. 2022.01.04.474967). p. 2022.01.04.474967. bioRxiv. https:// doi.org/10.1101/2022.01.04.474967 google scholar