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

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.
Competing Interests: Declaration of Competing Interest The authors have no commercial, proprietary or financial interest in the products or companies described in this article.
(Copyright © 2023 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.)