# US 2018/0024064 A1 · Method of detecting fibrous tissue in a biological specimen using co-localized images generated by second harmonic generation and two photon emission

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- Number: US 2018/0024064 A1
- Document ID: US20180024064A1
- Status: Published application
- Kind: A1
- Application number: 15/547,760
- Filed: 2016-03-18
- Published: 2018-01-25
- Earliest claimed: 2015-03-19
- Assignees: Histoindex Pte Ltd
- Applicants: Histoindex Pte Ltd
- Inventors: Gideon HO; Laurent GOLE; Weimiao YU
- Web page: https://patentgrep.com/patent/US20180024064A1

## CPC

- G01N 21/6486: {Measuring fluorescence of biological material, e.g. DNA, RNA, cells (G01N21/6428 takes precedence)}
- G01N 21/636: {using an arrangement of pump beam and probe beam; using the measurement of optical non-linear properties; (non-linear optics per se G02F1/35)}
- G01N 33/4833: {of solid biological material, e.g. tissue samples, cell cultures (tissue in vivo A61B5/00; cell suspensions G01N33/48735)}
- G02F 1/37: for second-harmonic generation {(G02F1/3532 takes precedence)}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/45: using co-occurrence matrix computation
- G01N 21/6458: {Fluorescence microscopy (fluorescence microscopes per se G02B21/0076 and G02B21/16)}
- G01N 2021/6439: {with indicators, stains, dyes, tags, labels, marks}
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06T 2207/10064: Fluorescence image
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G02F: OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

The present disclosure offers a method of detecting the presence of fibrous tissue deposited to a biological specimen or tissue sample. Preferably, the method comprises the steps of illuminating the specimen using an electromagnetic radiation of an excitation wavelength that the specimen contains excitable materials or compound to result in auto-fluorescence emitting a first electromagnetic signal caused by two-photon excitation, the specimen contains non-linear materials to generate a second electromagnetic signal in the form of second harmonic wave pursuant to the illuminating step; recording the first and the second electromagnetic signals emitted from the specimen; co-localizing the recorded first and second electromagnetic signals to generate an image; and detecting presence of the fibrous tissue deposited to the specimen using the generated image. More preferably, the light source is a laser beam or the like operable to induce two-photon excitation in the given tissue sample.

## Claims

### Claim 1

A method of image generating and analyzing for quantifying collagen fibers in a biological test specimen comprising:
irradiating an interested region of the test specimen using an electromagnetic light source through an optical assembly at an excitation wavelength, the irradiated test specimen having excitable materials and optically non-linear materials in the collagen fibers respectively leading to concurrent emission of a first electromagnetic signal caused by two-photon excitation (TPE) and a second electromagnetic signal as a result of second harmonic generation (SHG);
manipulating the first and second electromagnetic signals emitted from the test specimen through the optical assembly;
recording the manipulated first and the second electromagnetic signals through one or more sensors;
using the concurrently recorded first and second electromagnetic signals to generate a co-localized image having a set of image properties and being indicative of spatial distribution of the collagen fibers in the interested region; and
quantifying one or more scalar features and distribution features of the co-localized image to generate quantified result for each scalar and/or distribution feature, and deposition of collagen fibers in the test specimen using the quantified results for the extracted scalar and distribution features.

### Claim 2 (depends on 1)

The method of claim 1, wherein the distribution features of the co-localized image are any one or combinations of intensity of the second electromagnetic signal, distribution of the second electromagnetic signal, gray level co-occurrence matrix (GLCM), thickness of collagen fiber, length of the collagen fibers, orientation of the collagen fibers, and straightness of the collagen fibers.

### Claim 3 (depends on 1)

The method of claim 1, wherein the scalar features of the co-localized image are any one or combinations of collagen proportion ratio, total average collagen, complexity of collagen fibers, and fragment average ratio of the collagen fibers.

### Claim 4 (depends on 2)

The method of claim 2 further comprising the step of calculating at least one of mean, variance, skewness, kurtosis, energy and entropy for each of the distribution features.

### Claim 5 (depends on 3)

The method of claim 3, further comprising the step of deriving an absolute total average collagen by way of subjecting the co-localized image to multiple iterations of enhancement that each iteration of enhancement corresponds to a quotient of enhancement including N-fold amplification of the default pixel intensity of the co-localized image, calculating total average collagen for each iteration, generating a graft with the calculated total average collagen plotting against the quotient of enhancement, identifying a inflexion point from the plotted graph, and referring the total average collagen on the graph corresponding to the identified inflexion point to derive the absolute total average collagen, wherein N is 0.1 to 100.

### Claim 6 (depends on 5)

The method of claim 5 further comprising the step of identifying collagen fibers associated to blood vessels of the test specimen generated in the co-localized image and excluding the identified collagen fibers associated to blood vessels in deriving the absolute total average collagen.

### Claim 7 (depends on 5)

The method of claim 5 further comprising the step of mapping the generated graph against a standard graph calculated from a non-diseased specimen and deriving a prognosis towards a diseased state associated to the test specimen based upon relative distance between the mapped generated graph and the standard graph, wherein the generated and standard graphs are line graph.

### Claim 8 (depends on 5)

The method of claim 5 further comprising the step of deriving a prognosis towards a diseased state associated to the test specimen based upon a distance of the inflexion point of the generated graph in relation to a standard graph calculated from a non-diseased specimen.

### Claim 9 (depends on 1)

The method of claim 1, wherein the excitation wavelength is 700 to 850 nm.

### Claim 10 (depends on 1)

The method of claim 1, wherein the biological test specimen is trimmed to a thickness of 1 to 5 μm and free from any staining.

### Claim 11

An image generating and analyzing system for quantifying presence of collagen fibers in a biological test specimen comprising:
a platform for deposition of the test specimen thereon;
an electromagnetic light source coupling to the platform to direct a radiation at an excitation wavelength to an interested region of the test specimen, the radiation resulting in emission of a first and a second electromagnetic signals respectively caused by two-photon excitation (TPE) of excitable material and second harmonic generation (SHG) of optically non-linear materials in the collagen fibers in the test specimen;
a first sensor and a second sensor being arranged to respectively real-time record the emitted first and second electromagnetic signals and convert the received first and second electromagnetic signals separately into a first digital signal and a second digital signal;
an optical assembly for manipulating the radiation directed to the test specimen and the emitted first and second electromagnetic signals prior to having the first and second electromagnetic signals recorded by the first and second sensors;
a microprocessor unit being configured to use the first and second electromagnetic signals in generating a co-localized image having a set of image properties and being indicative of spatial distribution of the collagen fibers in the interested region, quantify one or more scalar features and distribution features of the co-localized image to generate quantified result for each scalar and/or distribution feature, quantifying collagen deposition in the test specimen using the quantified results for the extracted scalar and distribution features; and
a visual display, in communication with the microprocessor unit, providing an interface comprising one or more modules for customizing the set of adjustable parameters, displaying the co-localized image, quantified results of the scalar features and distribution features, and quantified collagen deposition.

### Claim 12 (depends on 11)

The system of claim 11, wherein the distribution features of the co-localized image are any one or combinations of intensity and/or distribution of the second electromagnetic signal, intensity and/or distribution of the first electromagnetic signal, gray level co-occurrence matrix (GLCM), thickness of collagen fiber, length of the collagen fibers, orientation of the collagen fibers, and straightness of the collagen fibers.

### Claim 13 (depends on 11)

The system of claim 11, wherein the scalar features of the co-localized image are any one or combinations of collagen proportion ratio, total average collagen, complexity of collagen fibers, and fragment average ratio of the collagen fibers.

### Claim 14 (depends on 12)

The system of claim 12, wherein the microprocessor unit is configured to compute at least one of mean, variance, skewness, kurtosis, energy and entropy for each of the distribution features.

### Claim 15 (depends on 13)

The system of claim 13, wherein the microprocessor unit is configured to compute an absolute total average collagen by way of subjecting the co-localized image to multiple iterations of enhancement that each iteration of enhancement corresponds to a quotient of enhancement including N-fold amplification of the default pixel intensity of the co-localized image, computing total average collagen for each iteration, generating a graft with the computed total average collagen plotting against the quotient of enhancement, identifying a inflexion point from the plotted graph, and referring the total average collagen on the graph corresponding to the identified inflexion point to derive the absolute total average collagen, wherein N is 0.1 to 100.

### Claim 16 (depends on 15)

The system of claim 15, wherein the microprocessor unit is configured to identify collagen fibers associated to blood vessels of the test specimen generated in the co-localized image and exclude the identified collagen fibers associated to blood vessels in deriving the absolute total average collagen.

### Claim 17 (depends on 15)

The method of claim 15, wherein the microprocessor unit is configured to map the generated graph against a standard graph computed from a non-diseased specimen and derive a prognosis towards a diseased state associated to the test specimen based upon relative distance between the mapped generated graph and the standard graph, wherein the generated and standard graphs are line graph.

### Claim 18 (depends on 15)

The system of claim 15, wherein the microprocessor unit is configured to derive a prognosis towards a diseased state associated to the test specimen based upon a distance of the inflexion point of the generated graph in relation to a standard graph computed from a non-diseased specimen.

### Claim 19 (depends on 11)

The system of claim 11, wherein the excitation wavelength is 700 to 850 nm.

### Claim 20 (depends on 11)

The system of claim 11, wherein the biological test specimen is trimmed to a thickness of 1 to 5 μm and free from any staining.