An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis

Oyibo, P.; Agbana, T.; Lieshout, L. van; Oyibo, W.; Diehl, J.C.; Vdovine, G.

doi:doi:10.1111/jmi.13269

Traditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in-focus image. However, these methods can be time-consuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in-focus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope,...Show moreTraditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in-focus image. However, these methods can be time-consuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in-focus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18% and 72.52% for the respective filter sizes. This advancement greatly supports the practicality of the Schistoscope in large-scale schistosomiasis monitoring and evaluation programs in endemic regions. This will save time, resources and also accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.Show less

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An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis

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