ArticlePloS one2022
Automated landmarking via multiple templates.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Do morphometric data improve phylogenetic reconstruction? A systematic review and assessment.BMC ecology and evolution · 2024Pooled it
- MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.F1000Research · 2026Article
- Anatomy-aware, label-informed approach improves image registration for challenging datasets.PloS one · 2026Article
- MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.ArXiv · 2025Article
- Differential regulation of coronal and lambdoid suture patency by PTHLH and HHIP activity in mice.Development (Cambridge, England) · 2025Article
- A Geometric Morphometrics Approach for Predicting Olfactory Region Accessibility: Toward Personalized Nose-to-Brain Drug Delivery.Journal of personalized medicine · 2025Article
- Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.Journal of anatomy · 2025Article
- Assessing the application of landmark-free morphometrics to macroevolutionary analyses.BMC ecology and evolution · 2025Article
- Article
- Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.bioRxiv : the preprint server for biology · 2024Article
- Increasing the impact of vertebrate scientific collections through 3D imaging: The openVertebrate (oVert) Thematic Collections Network.Bioscience · 2024Review
- Examining craniofacial variation among crispant and mutant zebrafish models of human skeletal diseases.Journal of anatomy · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multi-templates selection and MALPACA.
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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.