ArticleEvolutionary biology2020
A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics.
Article in Evolutionary biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Do morphometric data improve phylogenetic reconstruction? A systematic review and assessment.BMC ecology and evolution · 2024Pooled it
- Navigating the window: structural bottlenecks in avian craniofacial integration revealed by the ontogenetic patterning of shape variance.bioRxiv : the preprint server for biology · 2026Article
- The impact of alignment and scaling on biological inferences from landmark-free morphometrics.Journal of anatomy · 2026Article
- Application of Supervised Machine Learning Algorithms to Morphological Classification of Bufo bufo (Linnaeus, 1758) and B. verrucosissimus (Pallas, 1814) From Türkiye.Journal of morphology · 2026Article
- Integrating Genomics, Collections, and Community Science to Delimit Species Clarifies the Taxonomy of a Variable Monitor Lizard (Varanus tristis).Systematic biology · 2026Article
- Three-dimensional magnetic resonance imaging-based registration techniques and statistical shape analysis for knee osteoarthritis.Scientific reports · 2025Article
- Femur Shape Changes in Prg4-Deficient Mice: Morphological Insights Into Joint Well-Being.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Dosage-dependent effects of FGFR2Anatomical record (Hoboken, N.J. : 2007) · 2025Article
- Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.Journal of anatomy · 2025Article
- Carapace Morphological Characteristics of Chinese Mitten Crab (Animals : an open access journal from MDPI · 2025Article
- Assessing the application of landmark-free morphometrics to macroevolutionary analyses.BMC ecology and evolution · 2025Article
- Quantifying the relationship between cell proliferation and morphology during development of the face.Development (Cambridge, England) · 2025Article
- Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.bioRxiv : the preprint server for biology · 2024Article
- Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.Scientific reports · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Automatic landmark correspondence detection in medical images with an application to deformable image registration.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics.PLoS computational biology · 2023Article
- The genetic basis of neurocranial size and shape across varied lab mouse populations.Journal of anatomy · 2022Article
- MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.Scientific data · 2022Article
- Testing the accuracy of 3D automatic landmarking via genome-wide association studies.G3 (Bethesda, Md.) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Geometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. We combine techniques from image registration, geometric morphometrics, and deep learning to automate and optimize anatomical landmark detection. We test our method on high-resolution, micro-computed tomography images of adult mouse skulls. To ensure generalizability, we use a morphologically diverse sample and implement fundamentally different deformable registration algorithms. Compared to landmarks derived from conventional image registration workflows, our optimized landmark data show up to a 39.1% reduction in average coordinate error and a 36.7% reduction in total distribution error. In addition, our landmark optimization produces estimates of the sample mean shape and variance-covariance structure that are statistically indistinguishable from expert manual estimates. For biological imaging datasets and morphometric research questions, our approach can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.