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Huang SS, Lin YH, Wu SJ, Sung KB. Specific refraction-index increments of oxygenated hemoglobin from thalassemia-minor patients are not significantly different than those from healthy individuals. APPLIED OPTICS 2022; 61:9334-9341. [PMID: 36606879 DOI: 10.1364/ao.474991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/09/2022] [Indexed: 06/17/2023]
Abstract
The mass and concentration of hemoglobin per erythrocyte are important hematological parameters. Measuring these parameters from intact erythrocytes requires the value of specific refraction-index increment (RII) of oxygenated hemoglobin, which diverges in the literature. Refractive indices of hemoglobin solutions are measured directly by digital holographic microscopy on a microfluidic channel filled with hemoglobin solutions prepared by hemolysis of fresh human erythrocytes and refractive-index standards sequentially. Hemoglobin extracted from thalassemic patients shows 3-4% higher RII than that from healthy volunteers, but the difference is not significant in comparison to inter-subject variations within each group. The quantified RIIs are applied to quantify mean corpuscular hemoglobin mass of blood from 37 human subjects, and results are in accord with standard clinical test results.
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Wang W, Min L, Tian P, Wu C, Liu J, Hu XH. Analysis of polarized diffraction images of human red blood cells: a numerical study. BIOMEDICAL OPTICS EXPRESS 2022; 13:1161-1172. [PMID: 35414979 PMCID: PMC8973179 DOI: 10.1364/boe.445370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/26/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
We carried out a systematic study on cross-polarized diffraction image (p-DI) pairs of 3098 mature red blood cells (RBCs) using optical cell models with varied morphology, refractive index (RI), and orientation. The influence of cell rotation on texture features of p-DI pairs characterized by the gray-level co-occurrence matrix (GLCM) algorithm was quantitatively analyzed. Correlations between the transverse diameters of RBCs with different RI values and scattering efficiency ratios of s- and p-polarized light were also investigated. The correlations remain strong even for RBCs with significant orientation variations. In addition, we applied a minimum redundancy maximum relevance (mRMR) algorithm to improve the performance of support vector machine (SVM) classifiers. It was demonstrated that a set of selected GLCM parameters allowed for an efficient solution of classification problems of RBCs based on morphology. For 1598 RBCs with varied shapes corresponding to normal or pathological cases, the accuracy of the SVM based classifications increased from 83.8% to 96.8% with the aid of mRMR. These results indicate the strong potential of p-DI data for rapid and accurate screening examinations of RGC shapes in routine clinical tests.
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Affiliation(s)
- Wenjin Wang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Physics & Electronics Science and Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Li Min
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Physics & Electronics Science and Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Peng Tian
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Physics & Electronics Science and Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Chao Wu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Intelligent Manufacturing Research Institute, South-Central University for Nationalities, Wuhan, Hubei 430074, China
| | - Jing Liu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Xin-Hua Hu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Department of Physics, East Carolina University, Greenville, NC 27858, USA
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Lin YH, Liao KYK, Sung KB. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:JBO-200187R. [PMID: 33188571 PMCID: PMC7665881 DOI: 10.1117/1.jbo.25.11.116502] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/26/2020] [Indexed: 05/07/2023]
Abstract
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.
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Affiliation(s)
- Yang-Hsien Lin
- National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan
| | - Ken Y.-K. Liao
- Feng Chia University, College of Information and Electrical Engineering, Taichung, Taiwan
| | - Kung-Bin Sung
- National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan
- National Taiwan University, Department of Electrical Engineering, Taipei, Taiwan
- National Taiwan University, Molecular Imaging Center, Taipei, Taiwan
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