Vision-Based Tracking of Complex Macroparasites for High-Content Phenotypic Drug Screening
This paper proposes a method for vision-based automated tracking of schistosomula, the etiological agent of schistosomiasis, a disease which affects over 200 million people worldwide. The proposed tracking system is intended to facilitate high-throughput and high-content drug screening against the schistosomula by taking into account their complex phenotypic response to different candidate drug molecules. Our method addresses the unique challenges in tracking schistosomula, which include temporal changes in morphology, appearance, and motion characteristics due to the effect of drugs, as well as behavioral specificities of the parasites such as their tendency to remain stagnant in dense clusters followed by sudden rapid fluctuations in size and shape of individuals. Such issues are difficult to address using current bio-image tracking systems that have predominantly been developed for tracking simpler cell movements. We also propose a novel method for utilizing the results of tracking to improve the accuracy of segmentation across all images of the video sequence. Experiments demonstrate the efficacy of the proposed tracking method.
KeywordsVideo Sequence Motion Characteristic Segmentation Error Refinement Level Parasite Level
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