  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.

Computer Vision and Pattern Recognition;Neural and Evolutionary Computing