Reference : Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tr...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/218856
Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.
English
Vandaele, Rémy mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Aceto, Jessica [> >]
Muller, Marc mailto [Université de Liège - ULiège > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
Peronnet, Frederique [> >]
Debat, Vincent [> >]
Wang, Ching-Wei [> >]
Huang, Cheng-Ta [> >]
Jodogne, Sebastien [> >]
Martinive, Philippe mailto [Université de Liège - ULiège > Département des sciences biomédicales et précliniques > Département des sciences biomédicales et précliniques >]
Geurts, Pierre mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Marée, Raphaël mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2018
Scientific Reports
8
1
538
Yes (verified by ORBi)
International
2045-2322
United Kingdom
[en] Machine Learning ; Landmark detection ; Image processing
[en] 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.
http://hdl.handle.net/2268/218856
10.1038/s41598-017-18993-5

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