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：在药物开发与生产过程中，对主颗粒及纯度谱进行常规监测对于制造商避免产品变异和污染至关重要。透射电镜成像技术可帮助制造商预测工艺参数变化对基于病毒基因治疗载体产品及中间体的颗粒特征与纯度的影响。由于完整颗粒可表征高效产品，因此从混有碎片、破损颗粒及染色伪影的非完整病毒背景中自动检测完整腺病毒具有重要意义。在此类颗粒存在的情况下，完整腺病毒的检测难度显著增加。为克服这一挑战，我们开发了半自动腺病毒标注分割软件工具及基于透射电镜成像系统的完整腺病毒自动分割检测软件工具。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。定量与定性评估表明，我们的方法在假阳性率与假阴性率控制方面展现出卓越的真阳性检测率，能够准确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。