Fiche publication
Date publication
octobre 2022
Journal
Epigenomes
Auteurs
Membres identifiés du Cancéropôle Est :
Dr MUTTERER Jérôme
Tous les auteurs :
Johann To Berens P, Schivre G, Theune M, Peter J, Sall SO, Mutterer J, Barneche F, Bourbousse C, Molinier J
Lien Pubmed
Résumé
The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated () and one based on deep learning (), and their evaluation using a collection of nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.
Mots clés
automated segmentation, chromocenters, deep-learning, microscopy, nucleus
Référence
Epigenomes. 2022 10 5;6(4):
