Image-Analysis Based Readout Method For Biochip: Automated Quantification Of Immunomagnetic Beads, Micropads And Patient Leukemia Cell
| Autor: | Bulent Yilmaz, Kutay Icoz, Refika Sultan Dogan, Kasim Tasdemir, Fatma Uslu |
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| Přispěvatelé: | AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Cytological Techniques Protein Array Analysis General Physics and Astronomy Tumor cells Image processing 02 engineering and technology 01 natural sciences Execution time Automation Structural Biology Bright-field optical microscope 0103 physical sciences Digital image processing Image Processing Computer-Assisted Humans General Materials Science Biochip 010302 applied physics Leukemia Micron size Immunomagnetic Separation Immunomagnetic beads Cell Biology 021001 nanoscience & nanotechnology Object detection Leukemia cells 0210 nano-technology Micropads Biomedical engineering |
| Popis: | Authors acknowledge TUBITAK (Project No: 115E020) for financial support and Unal Akar for fabricating and preparing biochips. For diagnosing and monitoring the progress of cancer, detection and quantification of tumor cells is utmost important. Beside standard bench top instruments, several biochip-based methods have been developed for this purpose. Our biochip design incorporates micron size immunomagnetic beads together with micropad arrays, thus requires automated detection and quantification of not only cells but also the micropads and the immunomagnetic beads. The main purpose of the biochip is to capture target cells having different antigens simultaneously. In this proposed study, a digital image processing-based method to quantify the leukemia cells, immunomagnetic beads and micropads was developed as a readout method for the biochip. Color, size-based object detection and object segmentation methods were implemented to detect structures in the images acquired from the biochip by a bright field optical microscope. It has been shown that manual counting and flow cytometry results are in good agreement with the developed automated counting. Average precision is 85 % and average error rate is 13 % for all images of patient samples, average precision is 99 % and average error rate is 1% for cell culture images. With the optimized micropad size, proposed method can reach up to 95 % precision rate for patient samples with an execution time of 90 s per image. Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 115E020 |
| Databáze: | OpenAIRE |
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