| Full text | |
| Author(s): Show less - |
Cazzolato, Mirela T.
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Traina, Agma J. M.
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Boehm, Klemens
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Cuzzocrea, A
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Allan, J
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Paton, N
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Srivastava, D
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Agrawal, R
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Broder, A
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Zaki, M
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Candan, S
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Labrinidis, A
;
Schuster, A
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Wang, H
Total Authors: 14
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| Document type: | Journal article |
| Source: | CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT; v. N/A, p. 10-pg., 2018-01-01. |
| Abstract | |
Sequences of microscopic images feature the dynamics of developing embryos. Automatically tracking the cells from such sequences of images allows understanding the dynamics which a living element demands to know its cells movement, which ideally should take place in real-time. The traditional tracking pipeline starts with image acquisition, data transfer, image segmentation to separate cells from the background, and then the actual tracking step. To speed up this pipeline, we hypothesize that a process capable of predicting the cell motion according to previous observations is useful. The solution must be accurate, fast and lightweight, and be able to iterate between the various components. In this work we propose CM-Predictor, which takes advantage of previous positions of cells to estimate their motion. When estimation takes place, we can omit costly acquisition, transfer and process of images, speeding up the tracking pipeline. The designed solution monitors the error of prediction, adapting the model whenever needed. For validation, we use four different datasets with sequences of images with developing embryos. Then we compare the estimated motion vectors of CM-Predictor with traditional tracking methods. Experimental results show that CM-Predictor is able to accurately estimate the motion vectors. In fact, CM-Predictor maintains the prediction quality of other algorithms and performs faster than them. (AU) | |
| 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 |