Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network
| Autor: | Yang-Hsien Lin, Kung-Bin Sung, Ken Y.-K. Liao |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Paper
thalassemia Erythrocytes quantitative phase imaging Computer science Feature extraction Biomedical Engineering Holography digital holographic microscopy red blood cell 01 natural sciences Convolutional neural network Phase image 010309 optics Biomaterials 0103 physical sciences Microscopy business.industry Deep learning Pattern recognition Image segmentation Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Characterization (materials science) mask region-based convolutional neural network Erythrocyte Count Digital holographic microscopy Artificial intelligence Neural Networks Computer business Digital holography |
| Zdroj: | Journal of Biomedical Optics |
| ISSN: | 1560-2281 1083-3668 |
| Popis: | Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable. Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization. Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images. Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed. Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making. |
| Databáze: | OpenAIRE |
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