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Regular monitoring of the primary particles and purity profiles of a drugproduct during development and manufacturing processes is essential formanufacturers to avoid product variability and contamination. Transmissionelectron microscopy (TEM) imaging helps manufacturers predict how changesaffect particle characteristics and purity for virus-based gene therapy vectorproducts and intermediates. Since intact particles can characterize efficaciousproducts, it is beneficial to automate the detection of intact adenovirusagainst a non-intact-viral background mixed with debris, broken, and artefactparticles. In the presence of such particles, detecting intact adenovirusesbecomes more challenging. To overcome the challenge, due to such a presence, wedeveloped a software tool for semi-automatic annotation and segmentation ofadenoviruses and a software tool for automatic segmentation and detection ofintact adenoviruses in TEM imaging systems. The developed semi-automatic toolexploited conventional image analysis techniques while the automatic tool wasbuilt based on convolutional neural networks and image analysis techniques. Ourquantitative and qualitative evaluations showed outstanding true positivedetection rates compared to false positive and negative rates whereadenoviruses were nicely detected without mistaking them for real debris,broken adenoviruses, and/or staining artefacts.

Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles