Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images. (March 2019)

• Record Type: Journal Article
• Authors: Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Yilmaz, Bulent
• Is Part Of: Biomedical signal processing and control. Volume 49(2019)
• Page Start: 473
• Page End: 482
• Publication Date: 2019-03
• Subjects: Leukemia cells -- Image-processing -- Bright-field optical microscopy -- Machine learning -- Immunomagnetic beads -- Support vector machines; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.01.002
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Brightfield optical microscope images acquired by 20× and 40× objectives processed. No cell staining and no fluorescent labeling. Machine learning methods with color and size based object detection methods incorporated. Neural Network, Random Forest and Support Vector Machine methods compared. Detection and quantification of leukemia cells, micron size immunomagnetic beads and bead clusters performed on the images. Abstract: Quantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40× or a 20× objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40× objective and 79.7% for the 20× objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection.