deep learning in label free cell classification

deep learning in label free cell classification

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This work is licensed under a Creative Commons Attribution 4. Reprints and Permissions. Sci Rep 6, Download citation. Received : 06 August Accepted : 25 January Published : 15 March Nature Photonics FEBS Letters Biomedical Optics Express Health Informatics Journal By submitting a comment you agree to abide by our Terms and Community Guidelines.

If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Advanced search. Skip to main content. Subjects Biophotonics Cell biology Electrical and electronic engineering. Abstract Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. Download PDF. Introduction Deep learning extracts patterns and knowledge from rich multidimenstional datasets.

Figure 1: Time stretch quantitative phase imaging TS-QPI and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier EDFA , and a wavelength-division multiplexing WDM filter generate and shape a train of broadband optical pulses. Full size image. Feature Extraction The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig.

Table 1 List of extracted features. Full size table. Figure 3: Biophysical features formed by image fusion. Figure 4: Machine learning pipeline. Also, the sample size analyzed by these assays is limited due to their low throughput.

The discrepancy of the training and test errors is the generalization error of the learning model. Notice that beyond the generalization error does not decrease, and the learning curves converge to their ultimate performances. In other words, training data points are required to accomplish target achievable performance for the deep learning model used here in this example. Furthermore, the interquartile range of the balanced accuracy shown with box plot is the smallest for the regularized AUC-based deep learning model, which confirms its consistency and repeatability are the best among learning methods.

TS-QPI relies on spectral multiplexing to simultaneously capture both phase and intensity quantitative images in a single measurement, generating a wealth of information of each individual cell and eliminating the need for labeling with biomarkers. In this disclosure the information content of these images has been summarized in a set of 16 features for each cell, and classification performed in the hyperdimensional space composed of these features.

Applications of various learning algorithms were demonstrated, including deep neural networks, support vector machine, logistic regression, naive Bayes, as well as a new training method based on area under the ROC curve. The results from two experimental demonstrations, one on detection of cancerous cells among white blood cells, and another one on. The disclosed system opens the way to cellular phenotypic analysis as well as data-driven diagnostics, and thus, is a valuable tool for high-throughput label-free cell screening in medical, biotechnological, and research applications.

The following provides additional details regarding a specific example of this method. These filtered pulses then pass through an optical circulator and are coupled to free-space with a fiber collimator. A beam reducer shrank the rainbow beam six times with a pair of 90 degree off-axis parabolic gold-coated mirrors with reflected focal lengths of Reflective optics with parabolic gold-coated mirrors are utilized in these experimental demonstrations to minimize loss, aberration, and polarization sensitivity.

In the sample arm, the rainbow pulses pass through the cells and are reflected by the reflective substrate of the microfluidic device. In the reference arm, a dielectric mirror reflected the rainbow with a length mismatch with the sample arm causing spectral interference fringes, seen in FIG. Cells are hydrodynamically focused at the center of the channel flow at a velocity of 1. The reflected pulses from reference and sample arms were recombined at the beam splitter, compressed by the two diffraction gratings and coupled back into the fiber.

These return pulses were spectrally encoded by the spatial information of the interrogation field of view. As the envelope of the optical wave varies slowly in time compared to the period of the optical electromagnetic wave and the time mismatch between the reference arm and the sample arm, a slowly varying envelope approximation is emplo ed in the present analysis. The complex envelope of the input electric field, , is split into two arms of the Michelson interferometer at the beam splitter.

Note that can be simplified as when pulse shape is stable from pulse to pulse. The light split into the two arms of the. Into the sample arm:. Into the reference arm:. Optical intensity in the sample arm will be altered by the absorption and scattering of imaged cells, as well as that of the microfluidic channel and buffer solution. After passing through semi-transparent objects, not only will the electric field amplitude be modulated by the optical attenuation in the sample arm, but also the optical path length difference will lead to a phase shift, , induced by refractive index change caused by the object along the interrogation beam.

Thus, the complex fields of the light waves coming back to the beam splitter become:. From the sample arm:. From the reference arm:. Both and affect the optical field twice as each rainbow flash passes through the cell twice.

Since the is much smaller than the time scale of the envelope variations caused by the cell flow, we can approximate to be without sacrificing accuracy. The relative time delay of is defined compared to the central wavelength, , as , which is usually called intra-pulse time delay.

Written in terms of , Eq. D is the group velocity dispersion, that is, the temporal pulse spreading, , per unit bandwidth, per unit distance traveled. Thus the temporal samples of the energy flux absorbed at the photodetector are the intra-pulse concatenation of spectral samples followed by inter-pulse concatenation of pulse waveforms:.

Each expresses the th spectral spatial pixel at the th pulse line image. Applying Eq. One is , a. The amplitude of this envelope corresponds to the temporal shape of the optical pulse and its deviations caused by the object transmission as in brightfield microscopy.

It provides information about optical loss, for instance light absorption and scattering caused by surface roughness, granularity, and inner cell organelle complexity. This term can be separated by a bandpass filter, and its envelope can be derived by a nonlinear envelope detection technique.

After normalization to the envelope, the cosine com onent. The first term in cosine causes the interferogram fringe pattern. Since , it can be approximated as. A one-dimensional phase unwrapping algorithm followed by background phase removal gives the object phase shift, where corresponds to an empty pulse when no cell is in the field of view, for example background phase. Compared to the higher frequency components at 4.

During measurements when there is no Eq. The envelope and phase of the time-domain signal was first mapped into a series of spatial information , forming linescan bright-field and phase- contrast images, illuminated by the optical pulse at time. This is because within each optical pulse, the spatial information is mapped one-to-one into the spectral domain, , and spectrum is stretched in time, , where is the relative group delay time of each frequency component within a pulse with respect to the central wavelength.

These line-scan images based on , , ,… were then cascaded into a two dimensional image corresponding to , where the second dimension is the spatial mapping of time lapse based on object flow speed. The factor two 2 is to account for the fact that each wavelength component passes the cell twice in Michelson interferometer. It gives the temporal and spatial information of the combined effects from absorption and scattering:.

First of all, image noise reduction and smoothing have been performed, which can remove artifacts that are smaller than the optical resolution limit. Once objects are identified in the image, morphology of each single cell can be described by area, diameter, uniformity, aspect ratio, perimeter, number of surrounding clumped cells, and so forth. Intensity peaks of pixel brightness within each object are used to distinguish clumped objects.

The object centers are defined as local intensity maxima in the smoothed image. Retaining outlines of the identified objects helps validate and visualize the algorithm. In the next step, the objects touching the borders of the image, such as the edges of the field of view and data acquisition time window, are discarded. However, the chance of cells showing up at the edges is very low due to hydrodynamic focusing.

The disclosed technology is also capable of excluding dust, noise, and debris by neglecting the objects that are too small or have extreme aspect ratios. Size measurement of the. System and method for label-free identification and classification of biological samples. Systems and methods for generating an image of an inspection object using an attenuated beam.

Method and system for digital staining of label-free fluorescence images using deep learning. Segmenting 3d intracellular structures in microscopy images using an iterative deep learning workflow that incorporates human contributions.

Method and system for morphology based mitosis identification and classification of digital images. Methods and systems for identifying and localizing objects based on features of the objects that are mapped to a vector.

Method for predicting the future occurrence of clinically occult or non-existent medical conditions. Method and system for predicting resistance of a disease to a therapeutic agent using a neural network. It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning.

Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning.

This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files. Kikuchi , T. Kawabata , and 15H H.

Competing interests: A patent application on the label-free classification technology has been filed by Hamamatsu University School of Medicine and Hamamatsu Photonics K. There are no further patents, products in development or marketed products to declare.

Morphological classification of cells and tissue on a subcellular scale under a microscope has a long history in pathology, including cytology and histology. The subcellular organelles cause subcellular features such as increased nuclear-to-cytoplasmic ratio, granular cytoplasm, and a large round nucleus with prominent nucleolus [ 1 ].

In identifying and classifying diseases, physicians recognize and analyze the pattern features in the microscopic image and interpret its meaning from their past training experience.

In cell biology, cytology, and pathology, the features of cells or tissue to be recognized and analyzed can be enhanced in two ways: one is staining with dyes or labeling the molecules to be observed with fluorescence light; the other is optical filtering by dark- or bright-field microscopy, including label-free optical imaging such as phase-contrast and differential-interference-contrast imaging.

The former describes subcellular features as a distribution map of specific proteins or molecules. The later describes the features as a refractive index map of various proteins or molecules. In this paper, we refer to the refractive index map inside a cell as a subcellular structure. For the last decade, as one task in computer vision, pattern recognition has been the most-topical area in fields such as autonomous cars and security. In these fields, patterns on target objects are described in certain manners, such as Haar-like features, local binary patterns LBPs , and histogram of oriented gradients HOG [ 2 ][ 3 ], to enhance features of imagery or suppress artifacts such as illumination because general video or still cameras offer images as they are i.

The combination of pattern recognition and machine learning is opening up new fields in not only industry but also biomedical and medical imaging. Recently, as artificial intelligence becomes more advanced, automated diagnosis [ 4 ] of tissue and cells is gaining popularity. In such diagnosis, the texture of labeled or stained tissue slices and cells are automatically recognized and classified as normal or abnormal by computer vision trained by machine learning. On the other hand, label-free automated detection [ 5 — 8 ] and classification [ 9 — 13 ] of single cells not in tissue have been developed over the last decade or so.

For instance, cells have been classified via imaging-flow cytometry in a manner of bright-field microcopy except quantitative-phase microscopy QPM , dark-field microscopy, and machine learning of subcellular morphology [ 14 ]. Also, label-free drug assessment of cells is performed in the same manner as imaging-flow cytometry [ 15 ].

These two applications of imaging-flow cytometry utilize optical filtering to enhance the subcellular features of single cells. Here we refer to the physical parameters as cellular outlines to be contrasted with subcellular structures. A set of statistics of subcellular structures standard deviations, variances, skewness, kurtosis, and so on of a QPM image have been also utilized for cell classification by means of machine learning [ 24 , 25 ].

In consideration of pathological diagnosis, namely, a doctor diagnoses dysplasia of cells through a microscope on the basis of not only such cellular outline but also the subcellular structure of the tissue slices or cells stained, the subcellular structure of a single cell should also be extracted recognized and on the basis of which, cells are classified when computer vision also diagnoses. Among the various algorithms for extracting patterns from an image, histograms of oriented gradients [ 26 ] HOG is a de-facto standard for detecting humans by computer vision.

HOG is generally variant to image size and rotation. To the best of our knowledge, HOG-based feature extraction has not been applied to QPM images of label-free single-cells, although modified HOG has been used to extract features from an image of stained cells [ 27 ]. Alternative method for extracting patterns from an image and classifying them is deep-learning [ 28 ]. In this paper, we describe a shallow-learning [ 29 ] approach for classifying rule-based auto-segmented images of healthy white blood cells WBCs and cancer cell-lines CLs on the basis of the subcellular structure in QPM images.

After the sizes of the cells in the QPM images were longitudinally and laterally normalized, the subcellular structure was extracted by the HOG-based feature-extraction algorithm. A support vector machine [ 30 — 32 ] SVM classifier was trained on the subcellular features by HOG descriptor, and the classification performance was plotted as a detection-error trade-off curve [ 33 ] DET.

For comparison, another SVM classifier was also trained by using a set of statistics of subcellular structures statistical subcellular-structures. Among the various label-free imaging techniques, actively stabilized phase-shifting reflection-type QPM [ 34 — 37 ]—one kind of bright-field microcopy—was used in this study Fig 1 A. It provides quantitative morphological information about live cells without need for cytotoxic methods such as photo-bleaching and photo-toxicity [ 17 ], which are commonly used in fluorescence-labeled microscopy.

RI acts as an intrinsic contrast agent [ 38 ] that enhances contrast of transparent samples. In the reflection-type configuration of a QPM shown in Fig 1 C , light incident on the sample passes through the sample twice; therefore, according to Eq 1 , the physical path length PL is doubled.

In our QPM shown in Fig 1 A , the imaging light emitted from an LED center wavelength: nm is split into two lights: one is incident on the sample, and the other is modulated in phase by a mirror mounted on the PZT transducer. Magnification of our QPM is The theoretical depth of focus is 1. Tube lens with focal length of mm is used for the objective lens designed for a tube length of mm. Interferograms are acquired by a two-dimension-arrayed sensor CCD with a pixel size of 3.

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Adams , Paul I. High content screening: seeing is believing. Boddington , Elizabeth J. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

It is demonstrated that cells can be classified by pattern recognition of the tree structure of non-stained live cells, learnihg the pattern recognition was clasisfication by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. This label-free, non-cytotoxic cell classification based larning the subcellular structure of QPM images has the potential to classigication as an five nights at freddys silver eyes read online free diagnosis of single cells. This is deep learning in label free cell classification open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. KikuchiT. Kawabataand 15H H. Competing interests: A patent application on the label-free classification technology has been filed by Hamamatsu University School of Medicine and Hamamatsu Photonics K. There are no further patents, products in development or marketed products to declare. Morphological classification of cells and tissue on a subcellular scale under a microscope has a long history in pathology, including cytology and histology. The subcellular organelles cause subcellular features such as increased nuclear-to-cytoplasmic ratio, granular cytoplasm, and a large round nucleus with prominent nucleolus [ 1 deep learning in label free cell classification. In identifying and classifying diseases, physicians recognize and analyze the pattern features in the microscopic image and interpret its meaning from their past training experience. In cell biology, deep learning in label free cell classification, and pathology, the features of cells or tissue to be recognized and analyzed can be enhanced in two ways: one is staining with dyes or labeling the molecules to be observed with fluorescence light; the other is optical filtering by dark- or bright-field microscopy, including label-free optical classificaation such as phase-contrast derp differential-interference-contrast imaging. The former describes subcellular features as a distribution map of specific proteins deep learning in label free cell classification molecules. deep learning in label free cell classification PDF | Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of. ; doi: /srep Deep Learning in Label-free Cell Classification. Chen CL(1)(2), Mahjoubfar A(1)(2), Tai LC(2), Blaby IK(3). Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on. () Label-free classification of cells based on supervised machine learning of subcellular structures. PLoS ONE 14(1): e Claire's Scientific Reports classification paper. Contribute to rainingday/deep-​learning-in-label-free-cell-classification-paper development by creating an account. WOA1 * The Regents Of The University Of California Deep learning in label-free cell classification and machine vision. A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase. a natural fit to deep learning classification. Previously we had shown that high-​throughput label-free cell classification with high accuracy can. Keywords—Holography, Deep learning, Digital holography,. Machine learning, Image classification, Biological cells. I. INTRODUCTION. Cancer is a leading cause. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. The method as recited in claim 1, wherein the sensor comprises an image sensor. Ikeda, T. Each expresses the th spectral spatial pixel at the th pulse line image. Roc-based utility function maximization for feature selection and classification with applications to high-dimensional protease data. These attributes differ widely among cells and their variations reflect important information of genotypes and physiological stimuli. Since cells from even the same line or tissue exhibit variations in size, structure, and protein expression levels 44 , 45 , 46 , high accuracy classification can only be achieved by a model tolerant to these intrinsic variations. As a way to visualize the impact of the threshold on classification accuracy, a classifier that accurately separates the classes will have an ROC curve that approaches the upper left corner. Proceedings of the IEEE , — The relative net change of intensity envelope variations induced by the cell is obtained from the amplitude of the baseband intensity envelope of the interferogram as. Label-free high-throughput imaging flow cytometry. JOSA A 30 , — deep learning in label free cell classification