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Publication . Article . 2021

Object detection for automatic cancer cell counting in zebrafish xenografts

Carina Albuquerque; Leonardo Vanneschi; Roberto Henriques; Mauro Castelli; Vanda Póvoa; Rita Fior; Nickolas Papanikolaou;
Open Access
English
Published: 29 Nov 2021
Publisher: Public Library of Science
Abstract
Albuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021). Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609 -----------------------------------------This work was supported by national funds through FCT (Fundaçâo para a Ciência e a Tecnologia), under project PTDC/CCI-INF/29168/2017 (BINDER). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410). Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells’ size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set. publishersversion published
Subjects by Vocabulary

Universal Decimal Classification: udc:659.2:004

Microsoft Academic Graph classification: Artificial intelligence business.industry business Object detection Automation Cell counting Pattern recognition Process (computing) Data set Deep learning Feature (computer vision) Computer science Human error

Subjects

Multidisciplinary, Research Article, Physical Sciences, Mathematics, Geometry, Aspect Ratio, Medicine and Health Sciences, Oncology, Cancer Treatment, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Model Organisms, Zebrafish, Animal Models, Biology and Life Sciences, Organisms, Eukaryota, Animals, Vertebrates, Fish, Osteichthyes, Zoology, Imaging Techniques, Cancers and Neoplasms, Cell Enumeration Techniques, Applied Mathematics, Algorithms, Simulation and Modeling, Probability Theory, Probability Distribution, Skewness, General, SDG 3 - Good Health and Well-being, Medicine, R, Science, Q

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European Marine Science
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