Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning
Autor: | Siva A. Vanapalli, Hamed Sari-Sarraf, Golnaz Moallem, Anirudh Gangadhar, Adity A. Pore |
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Rok vydání: | 2021 |
Předmět: |
Microscopy
Computer science business.industry Deep learning Bright-field microscopy Biomedical Engineering Pattern recognition Breast Neoplasms Convolutional neural network Atomic and Molecular Physics and Optics Cell size Electronic Optical and Magnetic Materials Biomaterials Circulating tumor cell Deep Learning Cancer cell Cancer management Leukocytes Humans Female Breast cancer cells Artificial intelligence Neural Networks Computer business |
Zdroj: | Journal of biomedical optics. 27(7) |
ISSN: | 1560-2281 |
Popis: | SignificanceCirculating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization.AimThe goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs).ApproachWe tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN).ResultsBoth approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations.ConclusionsCNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples. |
Databáze: | OpenAIRE |
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