Measurement of the refractive index of whole blood and its components for a continuous spectral region

Liu, Shike; Deng, Zhichao; Li, Jianwei; Wang, Jin; Huang, Ningning; Cui, Ruiming; Zhang, Qiannan; Mei, Jianchun; Zhou, Wenyuan; Zhang, Chunping; Ye, Qing; Tian, Jianguo

doi:10.1117/1.jbo.24.3.035003

Cited by 57 publications

(33 citation statements)

References 28 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the collection efficiency of the fiber might be different after being polished at 15 . To check whether the collection efficiency changed after polishing, we calculated the reflected intensity ratios of each Intralipid-20% dilution over undiluted Intralipid-20% based on the data obtained using the flat and angle polished probes with Equations (7) and (8).…”

Section: Measurements On Turbid Mediamentioning

confidence: 99%

See 1 more Smart Citation

Refractive index measurement using single fiber reflectance spectroscopy

Zhang

Faber

Post

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

A method using single fiber reflectance spectroscopy to measure the refractive indices of transparent and turbid media over a broad wavelength range is presented and tested. For transparent liquid samples, the accuracy is within 0.2%, and the accuracy increases with increasing wavelength. For liquid turbid media, the accuracy is within 0.3% and increases with decreasing wavelength. For solid turbid samples, such as human skin, the accuracy critically depends on the optical contact between the fiber and sample surface. It is demonstrated that this technique has the potential to measure refractive indices of biological tissue in vivo.

show abstract

Section: Measurements On Turbid Mediamentioning

confidence: 99%

“…Commercial and modified Abbe refractometers are common instruments to measure the refractive indices of both transparent and turbid media . For optimal accuracy, these instruments usually have a narrow band light source to avoid dispersion by the instrument components.…”

Section: Introductionmentioning

confidence: 99%

Refractive index measurement using single fiber reflectance spectroscopy

Zhang

Faber

Post

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

“…Generally, the blood contains cells and a liquid portion known as the plasma [ 1 ]. The blood cells constitute about 45% of the blood volume, while the plasma constitutes the remaining 55% [ 2 , 3 ]. The blood cells are of three types that include the red blood cells ( erythrocytes ), white blood cells ( leukocytes ), and Platelets ( thrombocytes ) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Almezhghwi

Serte

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

show abstract

“…5 are lower than the RBCs in PBS in Fig. 4 due to the relatively high refractive index environment of blood serum and plasma in the tissue [17,39].…”

Section: Brain Imagingmentioning

confidence: 82%

Epi-mode tomographic quantitative phase imaging in thick scattering samples

Ledwig

Robles

2019

Biomed. Opt. Express

View full text Add to dashboard Cite

Quantitative phase imaging (QPI) is an important tool in biomedicine that allows for the microscopic investigation of live cells and other thin, transparent samples. Importantly, this technology yields access to the cellular and sub-cellular structure and activity at nanometer scales without labels or dyes. Despite this unparalleled ability, QPI's restriction to relatively thin samples severely hinders its versatility and overall utility in biomedicine. Here we overcome this significant limitation of QPI to enable the same rich level of quantitative detail in thick scattering samples. We achieve this by first illuminating the sample in an epi-mode configuration and using multiple scattering within the sample-a hindrance to conventional transmission imaging used in QPI-as a source of transmissive illumination from within. Second, we quantify phase via deconvolution by modeling the transfer function of the system based on the ensemble average angular distribution of light illuminating the sample at the focal plane. This technique packages the quantitative, real-time sub-cellular imaging capabilities of QPI into a flexible configuration, opening the door for truly non-invasive, label-free, tomographic quantitative phase imaging of unaltered thick, scattering specimens. Images of controlled scattering phantoms, blood in collection bags, cerebral organoids and freshly excised whole mouse brains are presented to validate the approach.

show abstract

Measurement of the refractive index of whole blood and its components for a continuous spectral region

Cited by 57 publications

References 28 publications

Refractive index measurement using single fiber reflectance spectroscopy

Refractive index measurement using single fiber reflectance spectroscopy

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Epi-mode tomographic quantitative phase imaging in thick scattering samples

Contact Info

Product

Resources

About