Object detection for automatic cancer cell counting in zebrafish xenografts

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

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.

• Jezik: Angleški jezik
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: 28 str.
• Številčenje: Vol. 16, iss. 11 (art. 0260609)
• PID: 20.500.12556/RUL-134809
• UDK: 659.2:004
• ISSN pri članku: 1932-6203
• DOI: 10.1371/journal.pone.0260609
• COBISS.SI-ID: 87543043
• Datum objave v RUL: 02.02.2022

Gradivo je del revije

• Naslov: PloS one
• Založnik: Public Library of Science
• ISSN: 1932-6203
• COBISS.SI-ID: 2005896

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 29.11.2021

Projekti

• Financer: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Številka projekta: PTDC/CCI-INF/29168/2017
• Naslov: BINDER

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Program financ.: Raziskovalni program
• Številka projekta: P5-0410
• Naslov: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe