Title :

A Hierarchical Deep Learning Framework for Nuclei 3D Reconstruction from Microscopic Stack-Images of 3D Cancer Cell Culture

Author(s) :

Maylaa, Tarek [Auteur correspondant]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Moulla Windal, Feryal [Auteur]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Benhabiles, Halim [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
JUNIA [JUNIA]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Maubon, Gregory [Auteur]
Maubon, Nathalie [Auteur]
Vandenhaute, Elodie [Auteur]
Collard, Dominique [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]

Conference title :

6th edition of the WorldS4 2022

City :

London

Country :

Royaume-Uni

Start date of the conference :

2022-08-24

Journal title :

Lecture Notes in Networks and Systems

Publisher :

Springer Nature

Publication place :

Singapore

Publication date :

2023-01-25

English keyword(s) :

3D cell culture
Confocal microscopy
z-stack images
Deep learning
Object detection
Segmentation
3D reconstruction

HAL domain(s) :

Physique [physics]
Sciences de l'ingénieur [physics]

English abstract : [en]

AbstractIn this article, we propose a hierarchical deep learning framework for the nuclei 3D reconstruction from a stack of microscopic images representing 3D cancer cell culture. The framework goes through three successive ...
Show more >AbstractIn this article, we propose a hierarchical deep learning framework for the nuclei 3D reconstruction from a stack of microscopic images representing 3D cancer cell culture. The framework goes through three successive stages namely: at the slice level of the stack (i) the spheroid detection and (ii) their nuclei segmentation then at the stack level (iii) the nuclei 3D reconstruction. For this purpose, we prepared a dataset of bright-field microscopic images acquired from 3D cultures of HeLa cells and manually annotated by the experts for both tasks (spheroids detection and nuclei segmentation). Two CNN models namely, YOLOv5x and U-Net-VGG19 have been trained and validated on our dataset for the detection and the segmentation tasks, respectively. For the 3D reconstruction task, the delaunay triangulation technique has been adopted by exploiting point cloud clusters that represent the segmented nuclei in the stack. Our framework offers to the biologists an efficient assisting tool for quantifying the number of spheroids and analyzing the morphology of their nuclei. The conducted experiments on our generated dataset show the promising results obtained by our framework with notably an average precision of 0.892 and 0.76 on the spheroids detection and nuclei segmentation respectively. Moreover, our 3D reconstruction technique shows visually a consistant representation of nuclei in term of volumetery and shapeShow less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL