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The benchmark data SET CeTReS.B-MI for in vitro mitosis detection

: Becker, T.; Kanje, W.; Rapoport, D.; Thierbach, K.; Scherf, N.; Roeder, I.; Madany Mamlouk, A.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Engineering in Medicine and Biology Society -EMBS-; IEEE Signal Processing Society:
IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Vol.1 : Beijing, China, 29 April - 2 May 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4673-1960-7
ISBN: 978-1-4673-1959-1
ISBN: 978-1-4673-1961-4
International Symposium on Biomedical Imaging (ISBI) <11, 2014, Beijing>
Conference Paper
Fraunhofer EMB ()

Mitosis detection poses a major challenge in cell tracking as mitoses are crucial events in the construction of genealogical trees. Making use of typical mitotic patterns that can be seen in phase contrast images of time lapse experiments, we propose a new benchmark data set CeTReS.B-MI consisting of mitotic and non-mitotic cells from the publicly accessible, fully labeled data set CeTReS.B. Using this data, two simple mitosis detectors (based on compactness and intensity) are used exemplarily to train, test and compare their ability to detect mitotic events. As a gold standard, we propose a linear support vector machine (SVM), which is able to separate the classes with a high accuracy (AUC=0.993). To illustrate the potential impact of a robust mitosis detection, the proposed classifiers are combined with two state of the art cell tracking algorithms. For both algorithms, performance does change when adding mitosis detection. Finally, this evaluation also emphasizes how easy implementation and comparison becomes, having suitable benchmark data at hand.