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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Establishing trajectories of moving objects without identities: The intricacies of cell tracking and a solution

Full text
Author(s):
Cazzolato, Mirela T. [1] ; Traina, Agma J. M. [1] ; Bohm, Klemens [2]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, USP Sao Carlos, Sao Carlos - Brazil
[2] Karlsruhe Inst Technol, Inst Program Struct & Data Org, KIT Karlsruhe, Karlsruhe - Germany
Total Affiliations: 2
Document type: Journal article
Source: INFORMATION SYSTEMS; v. 105, MAR 2022.
Web of Science Citations: 0
Abstract

Storing, querying, predicting, and interpolating trajectories of moving objects is a topic which the database community has studied for decades. We study a new variant of this problem in this article: We deal with a set of moving objects which do not have an identity, i.e., one does not know whether an object is identical to one observed earlier at another position. Our use case is a stream of images of cells of developing embryos. There exist so-called tracking tools. They match cells in such image sequences, to build trajectory vectors. However, these trackers have certain weaknesses, including counter-intuitive parameters and the expectation of users manually correcting trajectories. In this paper, we propose fully automatic tracking algorithms. They rely on space partitioning heuristics to match cells. This gives way to much cheaper data-analysis pipelines, as we will explain. We also propose two algorithms predicting the next positions of cells, given earlier ones. Experiments over 12 datasets show that our new approaches reduce the execution time by up to 7.8 times for tracking and 6.2 times for prediction. Prediction quality increases by up to 5.6% over the best tracker. (C) 2021 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 20/11258-2 - Interoperability and similarity queries on medical databases
Grantee:Mirela Teixeira Cazzolato
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
Grantee:Agma Juci Machado Traina
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
Grantee:Agma Juci Machado Traina
Support Opportunities: Regular Research Grants
FAPESP's process: 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD
Grantee:Mirela Teixeira Cazzolato
Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training