A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

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

Résumé

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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Dates et versions

hal-03793026 , version 1 (30-09-2022)

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Ziheng Yang, Halim Benhabiles, Feryal Windal, Jérôme Follet, Anne-Charlotte Leniere, et al.. A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images. Medical Optical Imaging and Virtual Microscopy Image Analysis. MOVI 2022, held in conjunction with MICCAI 2022, Sep 2022, Singapore, Singapore. pp.156-166, ⟨10.1007/978-3-031-16961-8_16⟩. ⟨hal-03793026⟩
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