20.500.12556/RUL-134809 Object detection for automatic cancer cell counting in zebrafish xenografts 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. true false true Angleški jezik Angleški jezik Članek v reviji 2022-02-02 15:10:14 2022-02-02 15:40:37 2022-09-14 04:10:03 0000-00-00 00:00:00 2021 0 0 28 str. iss. 11 (art. 0260609) Vol. 16 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 1932-6203 10.1371/journal.pone.0260609 87543043 RAZ_Albuquerque_Carina_2021.pdf RAZ_Albuquerque_Carina_2021.pdf 1 2D6024305AEFE227313F238E9977BEA5 e0a8285a58e843f28867b9fe4a25de3e1f58b45bf5fb95df2f0bcd80e96a425a c6d673b0-8432-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153470 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260609 0 cc4f01fe-8432-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153471 Ekonomska fakulteta 0 0 0