Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.

Semantics Machine Learning Image Processing, Computer-Assisted / methods
anomaly detection cell therapy manufacturing machine learning semantic segmentation

Journal

Cytotherapy
ISSN: 1477-2566
Titre abrégé: Cytotherapy
Pays: England
ID NLM: 100895309

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 22 04 2023
revised: 21 08 2023
accepted: 24 08 2023
medline: 27 11 2023
pubmed: 19 9 2023
entrez: 19 9 2023
Statut: ppublish

Résumé

Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost. One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities. This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

Sections du résumé

BACKGROUND AIMS OBJECTIVE
Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost.
METHODS METHODS
One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities.
RESULTS/CONCLUSIONS CONCLUSIONS
This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

Identifiants

DOI: 10.1016/j.jcyt.2023.08.011 PMID: 37725031
pubmed: 37725031
pii: S1465-3249(23)01042-3
doi: 10.1016/j.jcyt.2023.08.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1361-1369

Informations de copyright

Copyright © 2023 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors have no commercial, proprietary or financial interest in the products or companies described in this article.

Auteurs

Rui Qi Chen (RQ)

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

Benjamin Joffe (B)

Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.

Paloma Casteleiro Costa (P)

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.

Caroline Filan (C)

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.

Bryan Wang (B)

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.

Stephen Balakirsky (S)

Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.

Francisco Robles (F)

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.

Krishnendu Roy (K)

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.

Jing Li (J)

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. Electronic address: jli3175@gatech.edu.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Langage › Linguistique › Sémantique › Real-time semantic segmentation and anomaly...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Real-time semantic segmentation and anomaly...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › Real-time semantic segmentation and anomaly...