PUBLICATION
Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach
- Authors
- Vandaele, R., Aceto, J., Muller, M., Péronnet, F., Debat, V., Wang, C.W., Huang, C.T., Jodogne, S., Martinive, P., Geurts, P., Marée, R.
- ID
- ZDB-PUB-180113-9
- Date
- 2018
- Source
- Scientific Reports 8: 538 (Journal)
- Registered Authors
- Muller, Marc
- Keywords
- none
- MeSH Terms
-
- Algorithms
- Animals
- Body Weights and Measures/methods*
- Body Weights and Measures/standards
- Drosophila
- Humans
- Image Processing, Computer-Assisted/methods*
- Software
- Zebrafish
- PubMed
- 29323201 Full text @ Sci. Rep.
Citation
Vandaele, R., Aceto, J., Muller, M., Péronnet, F., Debat, V., Wang, C.W., Huang, C.T., Jodogne, S., Martinive, P., Geurts, P., Marée, R. (2018) Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. Scientific Reports. 8:538.
Abstract
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping