Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

• Published in: Scientific reports Vol. 8; no. 1; p. 538
• Main Authors: Vandaele, Rémy, Aceto, Jessica, Muller, Marc, Péronnet, Frédérique, Debat, Vincent, Wang, Ching-Wei, Huang, Cheng-Ta, Jodogne, Sébastien, Martinive, Philippe, Geurts, Pierre, Marée, Raphaël
• Format: Journal Article Web Resource
• Language: English
• Published: England Nature Publishing Group 11.01.2018; Nature Publishing Group UK
• Subjects: Algorithms; Animals; Body Weights and Measures - methods; Body Weights and Measures - standards; Computer Science; Drosophila; Engineering, computing & technology; Hominids; Humans; Image processing; Image Processing, Computer-Assisted - methods; Ingénierie, informatique & technologie; Landmark detection; Life Sciences; Machine Learning; Physics; Sciences informatiques; Software; Zebrafish
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-18993-5

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.