Abstract
In this paper, we present a deep learning-based framework for automated analysis and diagnosis of Cryptosporidium parvum from fluorescence microscopic images. First, a coarse segmentation is applied to roughly delimit the contours either of individual parasites or of grouped ones in the form of a single object from original images. Subsequently, a classifier will be applied to identify grouped parasites which are separated from each other by applying a fine segmentation. Our coarse-to-fine segmentation methodology achieves high accuracy on our generated dataset (over 3,000 parasites) and permit to improve the performance of direct segmentation approaches.
Supported by H4DC (Health for Dairy Cows) project.
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Funding
This project has received funding from the Interreg 2 Seas programme 2014–2020 co-funded by the European Regional Development Fund under subsidy contract No. 2S05-043 H4DC.
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Yang, Z., Benhabiles, H., Windal, F., Follet, J., Leniere, AC., Collard, D. (2022). A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images. In: Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (eds) Medical Optical Imaging and Virtual Microscopy Image Analysis. MOVI 2022. Lecture Notes in Computer Science, vol 13578. Springer, Cham. https://doi.org/10.1007/978-3-031-16961-8_16
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