Deep learning approaches to landmark detection in tsetse wing images
Fig 2
Flow chart of the entire procedure used in this study, including the tier 1 and 2 development and deployment.
The data were first recorded on paper and laminated with the physical tsetse wings, separated into volumes consisting of pages illustrated on the top left of the diagram. Biological lab recordings and tsetse wings were then digitally captured in a CSV file and nested folders (i.e. Vol/page) of images. This study used a subset of the full data set (Vol. 20 and 21) to establish a method for recording landmarks automatically. Labeller refers to the manual labelling stage to train machine learning models. Sample statistics were performed to understand the proportion of different categories of incomplete wings, which was used to inform an appropriate classification model. In addition, sample statistics were performed on misaligned pages to estimate the number of misalignment pages we expect to find. The tier 1 and 2 processes at the bottom of the figure explain the deployment process. Tier 1 decides whether a wing is complete and can be sent to tier 2, where landmarks are localised. The two-tier landmark detection system is deployed on the unannotated data set of all images in Volumes 20 and 21. The final Misalignment analysis is fully described in Fig A of S1 Fig.