细胞疗法是一种有前景的治疗方法，它利用活细胞来治疗各种疾病和病状，包括心血管疾病、神经系统疾病和某些癌症。随着对细胞疗法的兴趣的增长，有必要转向一种更高效、可扩展和自动化的制造过程，以以更低的成本生产高质量的产品。其中一种方法是使用非侵入性成像和实时图像分析技术来监测和控制制造过程。本研究提出了一种基于机器学习的图像分析流程，包括语义分割和异常检测功能。即使在给定有限的注释图像数据集的情况下，该方法也能轻松实施，能够分割细胞和碎片，并能识别污染或硬件故障等异常情况。 Copyright © 2023 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.

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