Tracking cell lineages in 3D by incremental deep learning - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue eLife Année : 2022

Tracking cell lineages in 3D by incremental deep learning

Résumé

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software's performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.
Fichier principal
Vignette du fichier
elife-69380-v1.pdf (3.93 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03799482 , version 1 (05-10-2022)

Identifiants

Citer

Ko Sugawara, Çağrı Çevrim, Michalis Averof. Tracking cell lineages in 3D by incremental deep learning. eLife, 2022, 11, ⟨10.7554/elife.69380⟩. ⟨hal-03799482⟩
15 Consultations
52 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More