Object detection for automatic cancer cell counting in zebrafish xenografts

Albuquerque, Carina (author), Vanneschi, Leonardo (author), Henriques, Roberto (author), Castelli, Mauro (author), Póvoa, Vanda (author), Fior, Rita (author), Papanikolaou, Nickolas (author)

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 auto- mation 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 demon- strate 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.

• Language: English
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 28 str.
• Numbering: Vol. 16, iss. 11 (art. 0260609)
• PID: 20.500.12556/RUL-134809
• UDC: 659.2:004
• ISSN on article: 1932-6203
• DOI: 10.1371/journal.pone.0260609
• COBISS.SI-ID: 87543043
• Publication date in RUL: 02.02.2022

Record is a part of a journal

• Title: PloS one
• Publisher: PLOS
• ISSN: 1932-6203
• COBISS.SI-ID: 2005896

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 29.11.2021

Projects

• Funder: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Project number: PTDC/CCI-INF/29168/2017
• Name: BINDER

• Funder: ARRS - Slovenian Research Agency
• Funding programme: Raziskovalni program
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe