Curated Dataset for Red Blood Cell Tracking from Video Sequences of Flow in Microfluidic Devices

Ivan Cimrák (), Peter Tarábek () and František Kajánek
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Ivan Cimrák: Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
Peter Tarábek: Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
František Kajánek: Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia

Data, 2023, vol. 8, issue 6, 1-9

Abstract: This work presents a dataset comprising images, annotations, and velocity fields for benchmarking cell detection and cell tracking algorithms. The dataset includes two video sequences captured during laboratory experiments, showcasing the flow of red blood cells (RBC) in microfluidic channels. From the first video 300 frames and from the second video 150 frames are annotated with bounding boxes around the cells, as well as tracks depicting the movement of individual cells throughout the video. The dataset encompasses approximately 20,000 bounding boxes and 350 tracks. Additionally, computational fluid dynamics simulations were utilized to generate 2D velocity fields representing the flow within the channels. These velocity fields are included in the dataset. The velocity field has been employed to improve cell tracking by predicting the positions of cells across frames. The paper also provides a comprehensive discussion on the utilization of the flow matrix in the tracking steps.

Keywords: convolutional neural network; red blood cells; object detection; blood cell tracking (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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