Regular monitoring of the primary particles and purity profiles of a drug product during development and manufacturing processes is essential for manufacturers to avoid product variability and contamination. Transmission electron microscopy (TEM) imaging helps manufacturers predict how changes affect particle characteristics and purity for virus-based gene therapy vector products and intermediates. Since intact particles can characterize efficacious products, it is beneficial to automate the detection of intact adenovirus against a non-intact-viral background mixed with debris, broken, and artefact particles. In the presence of such particles, detecting intact adenoviruses becomes more challenging. To overcome the challenge, due to such a presence, we developed a software tool for semi-automatic annotation and segmentation of adenoviruses and a software tool for automatic segmentation and detection of intact adenoviruses in TEM imaging systems. The developed semi-automatic tool exploited conventional image analysis techniques while the automatic tool was built based on convolutional neural networks and image analysis techniques. Our quantitative and qualitative evaluations showed outstanding true positive detection rates compared to false positive and negative rates where adenoviruses were nicely detected without mistaking them for real debris, broken adenoviruses, and/or staining artefacts.

翻译：在药品研发和生产过程中，定期监测药物制剂的主颗粒及纯度特征对于避免产品变异性与污染至关重要。透射电镜成像技术可帮助厂商预判工艺参数变化对病毒类基因治疗载体产品及中间体颗粒特性与纯度的影响。由于完整颗粒可表征有效产品，因此实现从混有碎片、破损及伪影颗粒的非完整病毒背景中自动检测完整腺病毒具有重要价值。此类干扰颗粒的存在显著增加了完整腺病毒的检测难度。为克服这一挑战，我们开发了两类软件工具：用于腺病毒半自动标注与分割的工具，以及基于透射电镜成像系统实现完整腺病毒自动分割与检测的工具。其中半自动工具采用传统图像分析技术，而自动工具则基于卷积神经网络与图像分析技术构建。定量与定性评估结果表明，我们的方法在真阳性检测率上显著优于假阳性与假阴性率，能够精准识别腺病毒而不将其误判为真实碎片、破损腺病毒或染色伪影。