ArticleJournal of biomedical optics2022
Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 25 citations in OpenAlex.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Review
- Exploring Label-Free Imaging Techniques with Copper Sulfide Microspheres for Observing Breast Cancer Cells.ACS omega · 2024Article
- Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.BMC biology · 2024Article
- Single-Cell Proliferation Microfluidic Device for High Throughput Investigation of Replicative Potential and Drug Resistance of Cancer Cells.Cellular and molecular bioengineering · 2023Article
- Comprehensive Profiling of Cancer-Associated Cells in the Blood of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy to Predict Pathological Complete Response.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood.RSC advances · 2023Article
- Synthesis and In Vitro Testing of YVOMolecules (Basel, Switzerland) · 2022Article
- Inertial focusing of particles and cells in the microfluidic labyrinth device: Role of sharp turns.Biomicrofluidics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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). APPROACH: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro 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 higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination 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 DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.
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