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Updated: Jul 16, 2025
Automated Counterflow Centrifugal System for Small-Scale Cell Processing
Published on: December 12, 2019
Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing
Rui Qi Chen1, Benjamin Joffe2, Paloma Casteleiro Costa3
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
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.
