CN111265187B - Noninvasive pathological diagnosis device and method for sjogren syndrome - Google Patents

Noninvasive pathological diagnosis device and method for sjogren syndrome Download PDF

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CN111265187B
CN111265187B CN202010069190.8A CN202010069190A CN111265187B CN 111265187 B CN111265187 B CN 111265187B CN 202010069190 A CN202010069190 A CN 202010069190A CN 111265187 B CN111265187 B CN 111265187B
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CN111265187A (en
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曾碧新
徐敏
林维豪
郭明柔
余康远
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Wenzhou Medical University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a noninvasive pathological diagnosis device and method for sjogren's syndrome, which solve the problems that the sjogren's syndrome is difficult to diagnose clinically, the examination period is long, and a traumatic operation is needed to obtain pathological sections. The method is based on a Single Snapshot multi-Frequency Demodulation algorithm-space Frequency Domain Imaging (SSMD-SFDI) system to image local tissues of a patient with sjogren syndrome, corresponding optical and physiological parameters are obtained by using an optical Imaging model mapped by a tissue layered structure, and a classifier obtained by training a support vector machine is used for predicting whether the patient has sjogren syndrome and the grade of the sjogren syndrome. The invention adopts non-invasive detection, can effectively distinguish healthy people from patients with xerosis syndrome, can more accurately predict the illness degree of the patients, has high detection efficiency and small volume, and is convenient for clinical measurement.

Description

Noninvasive pathological diagnosis device and method for sjogren syndrome
Technical Field
The invention relates to the field of medical treatment, in particular to a noninvasive pathological diagnosis device and method for sicca syndrome.
Background
Sjogren's Syndrome (SS) is a chronic inflammatory autoimmune disease that affects primarily the exocrine glands, also known as autoimmune exocrine gland epithelioitis or autoimmune exocrine disease. Clinically, besides the symptoms of dry mouth and dry eyes due to the damage and the reduced functions of the salivary gland and the lacrimal gland, there are symptoms of multi-system damage caused by the involvement of other exocrine glands and other organs except glands, such as skin, joints, kidneys, lungs, digestive system, nerves, blood system and the like, which have different degrees of pathological changes.
The disease is divided into primary and secondary, the primary sicca syndrome has unknown cause, and the secondary sicca syndrome is secondary to a definite connective tissue disease, such as systemic lupus erythematosus and rheumatoid arthritis. The prevalence rate of the disease in the population of China is 0.3-0.7%, and the prevalence rate in the elderly is 3-4%. The onset age of the disease is 40-50 years old, and the disease is mostly seen in women, and the ratio of men to women is 1: 9-20, also found in children. The disease is more latent in onset and has various clinical manifestations, and doctors have certain difficulties in diagnosing the disease and are easy to misdiagnose inexperienced doctors. At present, no radical treatment method exists, and measures are mainly taken to improve symptoms, control and delay the progress of tissue and organ damage caused by immune response and secondary infection. Therefore, it is important to detect and control the disease at an early stage.
The current accepted diagnostic criteria for sjogren's syndrome is the 2002 international classification (diagnostic) criteria for sjogren's syndrome. Items of classification criteria include oral symptoms, ocular signs, histological examination, salivary gland impairment, autoantibody (anti-SSA or anti-SSB) examination. The diagnosis standard is complex, the examination types are various, and various indexes need to be comprehensively analyzed. Both primary and secondary sjogren's syndrome require a definitive diagnosis that must include histological examination. Histological examination, i.e. lip gland biopsy, is performed by cutting lobular biopsy of lower lip part of patient under local anesthesia, and microscopic analysis is performed to observe the number of lymphocyte foci (4 mm) in unit area of pathological section 2 One focus is the infiltration number of the inner lymphocyte more than or equal to 50). The pathological grades of the lip gland biopsy are classified into I, II, III and IV according to Chisholm standard. In the case of less than one lymphocyte foci, a small number of lymphocyte infiltrates are grade i, a medium infiltrates are grade ii, there are one lymphocyte foci of grade iii, and two or more are grade iv. The biopsy of the labial gland has the defects that the operation is needed, the mind of a patient is panic, and an operation wound exists. After the operation, the doctor needs to process and analyze the pathological section, which consumes a lot of manpower and material resources and prolongs the whole diagnosis process.
Disclosure of Invention
In order to overcome the defects of the existing diagnosis technology of the sjogren syndrome, the invention provides a noninvasive pathological diagnosis device for the sjogren syndrome. Solves the problems that the sicca syndrome is difficult to diagnose clinically, the examination period is long, and a traumatic operation is needed to obtain pathological sections, and the like.
The technical scheme adopted by the invention is aimed at the sjogren syndrome, the non-invasive real-time dynamic space frequency domain imaging (SSMD-SFDI) is innovatively developed, and quantitative imaging such as the microstructure, the physical property, the local circulation and the like of biological tissues is realized by utilizing the interaction of light and the tissues.
The technical scheme adopted by the invention is as follows:
a non-invasive pathological diagnosis device for Sjogren's syndrome is characterized in that: the method comprises the following steps:
a hardware portion comprising an optical image acquisition device for acquiring an image of the oral mucosa inside the lower lip of a patient:
a software portion, the software portion comprising:
the image demodulation module is used for demodulating the acquired image into a Modulation Transfer Function (MTF) of the tissue;
the parameter inversion module is used for inverting and calculating the optical and physiological parameters reflecting the organizational structure and the microcirculation state by a Modulation Transfer Function (MTF);
and the pathology prediction module is used for predicting whether the xerosis syndrome exists or/and the xerosis syndrome grade by using the optical and physiological parameters reflecting the tissue structure and the microcirculation state.
Further, the optical image acquisition device comprises an RGB LED light source, a collimating lens, a grating, a projection objective, a film spectroscope, an imaging objective, a diaphragm and a CCD camera.
Further, in the image demodulation module, the Modulation Transfer Function (MTF) of the tissue is demodulated from the acquired image by a single snapshot multi-frequency demodulation algorithm (SSMD).
Further, the system also comprises an image preprocessing module which is used for preprocessing the image acquired by the optical image acquisition device, deducting the dark background, and selecting a proper single snapshot multi-frequency demodulation algorithm (SSMD) demodulation area and a final interested area (ROI).
And the model fitting module is used for predicting whether the subject suffers from the sjogren syndrome and the grade of the sjogren syndrome by using the classifier obtained by the training of the support vector machine.
Further, the optical parameters include: reduced scattering coefficient, scattering power, and the physiological parameters include total hemoglobin content, blood oxygen saturation, epithelial layer thickness, and tissue surface roughness.
The invention also provides a noninvasive pathological diagnosis method for sjogren's syndrome, which comprises the following steps:
(1) Image acquisition: imaging the lower lip oral mucosa tissue of the patient by an optical image acquisition device;
(2) Image preprocessing: preprocessing the acquired image, deducting a dark background, and selecting a proper SSMD demodulation area and a final interested area (ROI);
(3) Image demodulation: demodulating a Modulation Transfer Function (MTF) of the tissue by a single snapshot multifrequency demodulation algorithm (SSMD) on the selected proper SSMD demodulation area and the final interested area (ROI), wherein the MTF comprises optical characteristic information, namely an absorption coefficient (mua) and a constraint scattering coefficient (mus');
(4) And (3) parameter inversion: based on Monte Carlo or various scattering models and optical imaging models mapped by the tissue laminated structure, optical and physiological parameters such as reduced scattering coefficient, scattering capacity, total hemoglobin content, blood oxygen saturation, epithelial layer thickness, tissue surface roughness and the like of local tissues are calculated by MTF data inversion through a least square method, a table look-up method or other fitting methods;
(5) And (3) pathological prediction: and (3) training the obtained classifier through a support vector machine to predict whether the subject suffers from the sjogren syndrome and the grade of the sjogren syndrome.
The invention provides a brand-new photonic detection method for early diagnosis, treatment and curative effect tracking of sjogren's syndrome. The invention adopts non-invasive detection, so that the patient does not suffer from the pain of the lip gland biopsy operation. Meanwhile, the detection result can be obtained quickly, the detection efficiency and the accuracy are improved, convenience is brought to doctors and patients, and the method has important scientific and social significance.
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FIG. 1 is a schematic block diagram of an apparatus in an embodiment of the invention
FIG. 2 is a schematic diagram of an optical image capturing device according to an embodiment of the present invention
FIG. 3 is an optical imaging model of the layered structure mapping according to an embodiment of the present invention
FIG. 4 is a ROC chart of classification 2 of patients with grade I and II pathological after biopsy of normal human and labial gland in the example of the present invention
FIG. 5 is a class 2 ROC chart of normal, labial gland biopsy pathology grade I patient in the example of the present invention
FIG. 6 is a ROC chart of class 2 classification of patients with grade III and grade IV pathological biopsy of labial glands in an embodiment of the present invention
FIG. 7 is a table of the training results of the SVM in accordance with the embodiment of the present invention
Detailed Description
Fig. 1 shows a functional block diagram of an embodiment of a noninvasive pathological diagnosis apparatus for sjogren syndrome according to the present invention. The device is divided into a hardware part and a software part, wherein the hardware part is an optical image acquisition device, and the software part comprises a CCD parameter setting module, an image preprocessing module, an image demodulation module, a parameter inversion module, a model fitting module and a pathological grade prediction module.
Optical image acquisition device: SFDI is a novel non-contact, wide-field tissue structure imaging technique by impinging spatially modulated patterns of different spatial frequencies onto a sample area and capturing the reflected image with a CCD camera. The optical image acquisition schematic is shown in fig. 2. The mixed white light (composed of three monochromatic lights of red, green and blue) projected by the LED light source passes through the collimating lens and then becomes parallel light, then passes through a grating to form structured light, is amplified by the objective lens and then is projected onto a sample tissue through the thin film spectroscope, and then the back scattering light from a sample passes through the spectroscope, the imaging objective lens and the diaphragm and then is received by the CCD camera.
CCD parameter setting module: and after system parameters of the CCD camera are set, acquiring images of the sample tissues.
An image preprocessing module: preprocessing the acquired image, deducting dark background, and selecting a proper SSMD demodulation area and a final interested area (ROI).
An image demodulation module: the acquired picture is demodulated to obtain a Modulation Transfer Function (MTF) of the sample through a single snapshot multifrequency demodulation algorithm (SSMD), wherein the MTF comprises important optical characteristic information, namely an absorption coefficient (mua) and a constraint scattering coefficient (mus').
A parameter inversion module: based on Monte Carlo or various scattering models and optical imaging models mapped by the tissue laminated structure, fitting by a least square method, a table look-up method or other methods, and calculating optical and physiological parameters such as reduced scattering coefficient, scattering capacity, total hemoglobin content, blood oxygen saturation, epithelial layer thickness and the like of local tissues by MTF data inversion.
By analyzing the parameters, the obtained variation trend of each parameter is consistent with clinical performance, and the feasibility of the device is proved.
A model fitting module: and (3) training the obtained classifier through a support vector machine to predict whether the subject has the sjogren's syndrome and the grade of the sjogren's syndrome. When a support vector machine is used for carrying out two-classification (healthy person VS xerosis syndrome patients), the healthy person and the xerosis syndrome patients can be effectively distinguished; when the support vector machine is used for five classifications (healthy people, I, II, III and IV), all levels of patients of healthy people and patients with xerosis syndrome can be distinguished more accurately.
A pathology grade prediction module: the method has the advantages that a local tissue picture of an unknown object is collected, new optical and physiological parameters are obtained after data processing, and whether the object has the sicca syndrome or not and the pathological grade of the sicca syndrome can be reasonably and accurately predicted.
Setting image acquisition parameters of the CCD, selecting proper light intensity of the light source, and utilizing the optical image acquisition device to image the lower lip oral mucosa of a healthy person and a xerosis syndrome patient respectively. Then, preprocessing the acquired image, selecting a proper range to perform SSMD demodulation on the image, then selecting a small-range region of interest (ROI) of each image to perform data analysis, obtaining MTF direct current components and alternating current components of the region, and solving the mean value of the MTF direct current components and the alternating current components to represent the average state of tissues in the region. Based on a Monte Carlo or various scattering models and an optical imaging model mapped by a tissue laminated structure, optical and physiological parameters such as reduced scattering coefficient, scattering capacity, total hemoglobin content, blood oxygen saturation, epithelial layer thickness, tissue surface roughness and the like of local tissues are calculated by MTF data inversion through a least square method, a table look-up method or other fitting methods. Compared with a three-step phase shifting method, the novel single-snapshot multi-frequency demodulation method (SSMD) is high in speed and higher in demodulation precision, the requirement for acquiring multiple frequency information at one time is met, and dynamic measurement of optical parameters is realized. The method can extract Alternating Current (AC) and Direct Current (DC) components from a structured light image simultaneously containing a plurality of modulation frequencies by fully utilizing the orthogonality of harmonic functions.
Assuming that the modulated light pattern incident on the tissue sample and the resulting light intensity pattern from tissue backscattering are represented by equations (1) and (2) respectively,
Figure BDA0002376869370000041
Figure BDA0002376869370000042
wherein k ≧ 1 is the number of alternating-current components,
Figure BDA0002376869370000043
and I AC,i Respectively at spatial frequency (f) x,i ,f y,i ) And phase phi i The incident amplitude and backscatter amplitude of the ith alternating current component below.
The key to SSMD is to select k different spatial frequencies (take f) x,i ＝m i /T 1 And f y,i ＝n i /T 2 ) wherein-T 1 <m i <T 1 ,-T 2 <n i <T 2 ，m i ,n i ,T 1 And T 2 Are all integers. In other words, T 1 And T 2 The least common multiple of all ac modulation spatial periods along the horizontal and vertical directions is defined separately. As in the standard three-phase shift modulation method, further assume I AC,i (x, y) is slowly varying and can be considered to be at T 1 ×T 2 The sliding window is a constant. Each exchanges minuteMagnitude of quantity I AC,i And the amplitude of the DC component I DC Calculated by the formulas (3) and (4) respectively,
Figure BDA0002376869370000051
Figure BDA0002376869370000052
in which the integration process is performed over a sliding window sigma. Here, in order to eliminate the phase phi i Respectively, using a spatial modulation frequency of (f) x,i ,f y,i ) And a size of T 1 ×T 2 I (x, y) and the resulting two images are combined to obtain the corresponding AC component.
The chromophore predominates in the oral mucosa is oxygenated hemoglobin (HbO) 2 ) And hypoxic hemoglobin (Hb), since the melanin content of the oral mucosal epithelium is much less than the hemoglobin content of the lamina propria, we ignore the absorption of melanin in the epithelium, considering only the absorption of oxygenated and hypoxic hemoglobin in the lamina propria. The equation is established as follows:
Figure BDA0002376869370000053
wherein epsilon Hb 、
Figure BDA0002376869370000054
Is the molar extinction coefficient of anoxic hemoglobin, oxygenated hemoglobin, c Hb 、/>
Figure BDA0002376869370000055
Is the concentration of hypoxic and oxygenated hemoglobin, and λ is the wavelength. According to the absorption coefficient, the content of oxygen-containing hemoglobin and oxygen-free hemoglobin of local tissues in the treatment area can be obtained, so that the total blood volume and the blood oxygen saturation of the tissues are deduced。
The bilayer structure of the oral mucosa is mapped onto a homogeneous medium of equivalent absorption, resulting in the following relation:
μ a (q，λ)L(q，λ)＝μ a，laminapropria (λ)(L-h) (6)
the diffuse reflectance of the equivalent uniform medium is equal to the sum of the diffuse reflectance of each layer.
Figure BDA0002376869370000056
Where q =2 pi f, f is the spatial modulation frequency, and the contribution of each layer is:
Figure BDA0002376869370000057
wherein
Figure BDA0002376869370000058
According to the formula, a reduced scattering coefficient of each layer can be obtained, the epithelial roughness influences the scattering coefficient in the diffuse reflection process, and the epithelial roughness and the scattering capacity of each layer can be deduced according to the reduced scattering coefficient.
Figure BDA0002376869370000059
L is the extrapolation length, and the depth of investigation L
Figure BDA00023768693700000510
From this equation we can derive the depth of detection L of light in the tissue, and from the laminar equivalent model of biological tissue we derive the thickness of the epithelial layer (as shown in fig. 3).
Then, the optical characteristic parameters and the physiological parameters of 14 healthy people, 44 patients with grade I labial gland biopsy, 35 patients with grade II labial gland biopsy, 41 patients with grade III labial gland biopsy and 48 patients with grade IV labial gland biopsy are analyzed, the change trend of the optical characteristic parameters and the physiological parameters is consistent with the clinical expression trend of the sjogren syndrome, all data are subjected to machine learning through a support vector machine, the results of two classification (healthy people VS patients with dry syndrome) and five classification (healthy people, I, II, III and IV) (see fig. 7 and fig. 4-6) are obtained, the healthy people and the patients with dry syndrome can be effectively distinguished, and the grade of the sjogren syndrome is distinguished.
Analysis of Experimental results
By analyzing the clinical features of sjogren's syndrome in the blood system, it is mainly manifested as cytopenia. Blood cells are mainly classified into erythrocytes, leukocytes, and platelets. Hemoglobin is the major component of red blood cells, and thus, it can be found that the total blood volume is continuously decreased as the disease progresses.
The pathological features of sjogren's syndrome in the lung are interstitial lesions, and a small proportion of patients have pulmonary hypertension and pulmonary fibrosis, and these pulmonary diseases can cause the oxygen carrying capacity of hemoglobin to be reduced, so that the reduction of the blood oxygen saturation in local tissues of oral mucosa can be observed.
The xerosis syndrome patient is easy to be infected by fungi due to the reduction of salivary secretion, so that inflammatory reaction is caused, pseudomembranous or erythema mucosal lesion appears on the mucosal surface, the pseudomembrane is a gray-white membranous substance formed by aggregation of inflammation exudation cellulose, desquamated epithelial cells and inflammatory cells, and the erythema mucosal lesion is represented by bright red plaque appearing on oral mucosa and belongs to proliferative lesion. Thus, the thickness of the epithelium increases slightly in patients with sjogren's syndrome.
Due to the pathological changes of salivary glands, the amount of saliva secreted by the patient is reduced, the surface of the oral mucosa becomes dry, and the roughness of the surface is increased.
Compared with the oral mucosa epithelial cells of the dry syndrome patient, the nucleus-cytoplasm ratio of the cells is increased, the number of the cells in a unit area is increased, and therefore the reduction scattering coefficient of the epithelial layer is increased.

Claims (6)

1. A noninvasive pathological diagnosis device for sjogren syndrome is characterized in that: the method comprises the following steps: a hardware portion including an optical image capture device for obtaining an image of the oral mucosa inside the lower lip of a patient: a software portion, the software portion comprising: the image demodulation module is used for demodulating the acquired image into a Modulation Transfer Function (MTF) of the tissue; the parameter inversion module is used for inverting and calculating optical and physiological parameters reflecting the tissue structure and the microcirculation state by a Modulation Transfer Function (MTF); and the pathology prediction module is used for predicting whether the xerosis syndrome exists or/and the grade of the xerosis syndrome by using the optical and physiological parameters reflecting the tissue structure and the microcirculation state.
2. The non-invasive pathological diagnosis apparatus for sjogren syndrome according to claim 1, characterized in that: the optical image acquisition device comprises an RGB LED light source, a collimating lens, a grating, a projection objective, a film spectroscope, an imaging objective, a diaphragm and a CCD camera.
3. The non-invasive pathological diagnosis apparatus for sjogren syndrome according to claim 1, characterized in that: in the image demodulation module, a Modulation Transfer Function (MTF) of the tissue is demodulated from the acquired image by a single snapshot multi-frequency demodulation algorithm (SSMD).
4. The non-invasive pathological diagnosis apparatus for sjogren syndrome according to claim 1, characterized in that: the system also comprises an image preprocessing module which is used for preprocessing the image acquired by the optical image acquisition device, deducting the dark background, and selecting a proper single snapshot multi-frequency demodulation algorithm (SSMD) demodulation area and a final interested area (ROI).
5. The non-invasive pathological diagnosis apparatus for sjogren syndrome according to claim 1, characterized in that: the model fitting module is used for predicting whether the subject suffers from the sjogren syndrome and the grade of the sjogren syndrome through the classifier obtained by the training of the support vector machine.
6. The non-invasive pathological diagnosis device for sjogren syndrome according to claim 1, characterized in that: the optical parameters include: reduced scattering coefficient, scattering power, and the physiological parameters include total hemoglobin content, blood oxygen saturation, epithelial layer thickness, and tissue surface roughness.
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