Chen RQ, Joffe B, Casteleiro Costa P, Filan C, Wang B, Balakirsky S, Robles F, Roy K, Li J. Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.
Cytotherapy 2023;
25:1361-1369. [PMID:
37725031 PMCID:
PMC10719834 DOI:
10.1016/j.jcyt.2023.08.011]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/21/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
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.
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