Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.
Cytotherapy
; 25(12): 1361-1369, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37725031
ABSTRACT
BACKGROUND AIMS:
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:
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:
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Semântica
/
Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
Cytotherapy
Assunto da revista:
TERAPEUTICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos