Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study - Institut des sciences de la Terre ( Grenoble) Accéder directement au contenu
Article Dans Une Revue Scientific Reports Année : 2023

Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study

Résumé

Abstract The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples (out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. Moreover, these videos are of different durations (from 6 to more than 100 frames). We present a two-stage end-to-end machine learning pipeline able to automatically classify cell motions in videos with a high class imbalance. By extending, comparing, and combining two state-of-the-art methods, a convolutional neural network (CNN) model and a recurrent CNN, we are able to automatically discard 97% of the unreliable cell sequences (first stage) and classify highly and poorly deformable red cell sequences with 97% accuracy and an F1-score of 0.94 (second stage). Dataset and codes are publicly released for the community.
Fichier principal
Vignette du fichier
s41598-023-27718-w.pdf (1.45 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
licence : CC BY - Paternité

Dates et versions

hal-03956053 , version 1 (25-01-2023)

Licence

Paternité

Identifiants

Citer

Maxime Darrin, Ashwin Samudre, Maxime Sahun, Scott Atwell, Catherine Badens, et al.. Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study. Scientific Reports, 2023, 13 (1), pp.745. ⟨10.1038/s41598-023-27718-w⟩. ⟨hal-03956053⟩
50 Consultations
18 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More