A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images | Yang, Ziheng
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A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images

Archive ouverte : Communication dans un congrès

Yang, Ziheng | Benhabiles, Halim | Windal, Feryal | Follet, Jérôme | Leniere, Anne-Charlotte | Collard, Dominique

Edité par HAL CCSD ; Springer Nature Switzerland

poster. International audience. 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.

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