CN114366037B - Spectral diagnosis system for guiding in brain cancer operation and working method thereof - Google Patents
Spectral diagnosis system for guiding in brain cancer operation and working method thereof Download PDFInfo
- Publication number
- CN114366037B CN114366037B CN202210140944.3A CN202210140944A CN114366037B CN 114366037 B CN114366037 B CN 114366037B CN 202210140944 A CN202210140944 A CN 202210140944A CN 114366037 B CN114366037 B CN 114366037B
- Authority
- CN
- China
- Prior art keywords
- data
- unit
- warning
- tumor
- light source
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 208000003174 Brain Neoplasms Diseases 0.000 title claims abstract description 23
- 238000003745 diagnosis Methods 0.000 title abstract description 14
- 230000003595 spectral effect Effects 0.000 title abstract description 11
- 238000000034 method Methods 0.000 title abstract description 10
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 51
- 238000001228 spectrum Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 25
- 239000000523 sample Substances 0.000 claims abstract description 15
- 230000005284 excitation Effects 0.000 claims description 37
- 230000003287 optical effect Effects 0.000 claims description 22
- 239000013307 optical fiber Substances 0.000 claims description 16
- 238000002189 fluorescence spectrum Methods 0.000 claims description 13
- 238000001356 surgical procedure Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 10
- 238000011002 quantification Methods 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 claims description 5
- 238000011161 development Methods 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 4
- 210000000056 organ Anatomy 0.000 claims 1
- 210000005013 brain tissue Anatomy 0.000 abstract description 10
- 238000001514 detection method Methods 0.000 abstract description 8
- 238000001727 in vivo Methods 0.000 abstract description 8
- 230000036285 pathological change Effects 0.000 abstract description 2
- 231100000915 pathological change Toxicity 0.000 abstract description 2
- 206010018338 Glioma Diseases 0.000 description 7
- 238000010801 machine learning Methods 0.000 description 6
- 239000000835 fiber Substances 0.000 description 5
- 230000003902 lesion Effects 0.000 description 5
- 238000012549 training Methods 0.000 description 4
- 238000010827 pathological analysis Methods 0.000 description 3
- 208000032612 Glial tumor Diseases 0.000 description 2
- 238000013145 classification model Methods 0.000 description 2
- 208000005017 glioblastoma Diseases 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000007917 intracranial administration Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000000985 reflectance spectrum Methods 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- 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
- A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
-
- 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
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The spectral diagnosis system and the working method for guiding in the brain cancer operation can give out normal and pathological change diagnosis results in real time or near real time, and the detection probe can easily reach the operation area to meet the in-vivo diagnosis requirement in the operation, and has no damage to brain tissues in the in-vivo use. The control unit controls the laser light source unit and the spectrum acquisition unit to cooperatively work to acquire spectrum data, and the data processing unit and the intraoperative early warning unit are combined to make tumor level early warning for an intraoperative surgeon; the data processing unit is connected with the control unit and the intraoperative warning unit, the control unit inputs the spectrum data into the data processing unit, and the data processing unit outputs the result to the intraoperative warning unit after analyzing the data.
Description
Technical Field
The invention relates to the technical field of photoelectric detection, in particular to a spectral diagnosis system used for guiding in brain cancer operation and a working method of the spectral diagnosis system used for guiding in brain cancer operation.
Background
Glioma is a brain tumor which is quite common in clinic and is characterized in that: 1) Gliomas have no clear tumor boundaries; 2) Gliomas have a strong permeability, and malignant gliomas penetrate into the surrounding normal tissues; 3) Gliomas grow very rapidly with a survival time of about 12-15 months. Gliomas can be classified into four classes according to the glioma classification criteria of the world health organization, with Glioblastoma (GBM) having high permeability and growth rate, requiring resection or treatment as soon as possible to extend the survival of the patient. In intracranial surgery, the surgeon can only rely on experience and preoperative medical images to determine the extent of the tumor margin in combination with the results of intra-operative frozen sections in order to ensure maximum margin. Conventional frozen sections are time consuming and may also suffer from sampling errors. There is a great need for an intraoperative diagnostic method for surgeons that has the following characteristics: 1) The diagnostic equipment needs to realize rapid detection of brain tissues in operation, and can give out normal and pathological diagnosis results in real time or near real time; 2) The diagnostic equipment, particularly the detection probe of the equipment, can be miniaturized, so that the detection probe can easily reach an operation area, and the in-vivo diagnostic requirement in operation is met; 3) No damage to brain tissue is caused when the composition is used in a body.
Disclosure of Invention
In order to overcome the defects of the prior art, the technical problem to be solved by the invention is to provide a spectral diagnosis system for guiding in brain cancer operation, which can give out normal and pathological change diagnosis results in real time or near real time, and the detection probe can easily reach an operation area to meet the in-vivo diagnosis requirement in operation, and has no damage to brain tissues in use.
The technical scheme of the invention is as follows: such a spectroscopic diagnostic system for guiding in brain cancer surgery, comprising:
the device comprises an excitation light source unit, a spectrum acquisition unit, a control unit, a data processing unit and an intraoperative warning unit;
the control unit controls the laser light source unit and the spectrum acquisition unit to cooperatively work to acquire spectrum data, and the data processing unit and the intraoperative early warning unit are combined to make tumor level early warning for an intraoperative surgeon; the data processing unit is connected with the control unit and the intraoperative warning unit, the control unit inputs the spectrum data into the data processing unit, and the data processing unit outputs the result to the intraoperative warning unit after analyzing the data;
the data processing unit comprises a deep learning development board (5) which calculates weave optical parameters of 3 groups of different light source-detector distances from 3 groups of diffuse reflection spectrum data through inversion operation of Monte Carlo; inputting the 3 groups of tissue optical parameters and the 3 groups of endogenous fluorescence spectrum data with the same light source-detector distance into a fluorescence quantification algorithm to obtain 3 groups of fluorescence quantification data;
respectively inputting 3 groups of tissue optical parameters and 3 groups of fluorescence quantitative data into a multi-layer perceptron model to obtain tumor grading prediction, wherein the multi-layer perceptron model can grade a data set into high-grade tumor, low-grade tumor and normal tissue; the data processing unit will obtain 3 sets of tissue optical parameters and 3 sets of predictions of fluorescence quantitative data for each execution.
The invention can combine the diffuse reflection spectrum with the endogenous fluorescence spectrum, detect the tissue optical characteristics and the endogenous fluorescence information of the target focus, and grade the tumor of the target focus by using a machine learning algorithm, thereby being capable of giving out normal and pathological diagnosis results in real time or near real time, and the detection probe can easily reach an operation area to meet the in-vivo diagnosis requirement in operation, and has no damage to brain tissues in-vivo use.
There is also provided a method of operating a spectroscopic diagnostic system for guidance in brain cancer surgery, comprising the steps of:
(1) And (3) data acquisition: the multimode optical fiber is connected with the spectrometer and the excitation light source, and the spectrometer and the excitation light source are connected with the computer by using the USB controller. Aiming the multimode fiber probe at a target focus, and acquiring diffuse reflection spectrum and endogenous fluorescence spectrum by using data acquisition software based on a LabVIEW system;
(2) Calculating optical parameters and fluorescence quantitative data of the tissue: extracting tissue optical parameters of a target focus by using the diffuse reflection spectrum data obtained in the step (1), and performing fluorescence quantitative operation by using the spectrum data of the endogenous fluorescence obtained in the step (1) to obtain fluorescence quantitative data;
(3) Training a machine learning model: using the tissue optical parameters and fluorescence quantitative data obtained in the step (2) as a data set, inputting the data set into a machine learning model for model training, determining an optimal threshold value of a classification model, and dividing a target focus into the following three levels: normal tissue, low grade tumor, high grade tumor;
(4) The system gives a warning prompt to the clinical surgeon in operation according to the grade of the target focus obtained in the step (3).
Drawings
Fig. 1 shows a schematic structure of a spectroscopic diagnostic system for guiding in brain cancer surgery according to the present invention.
Fig. 2 shows a front view of an excitation light source and a spectrometer for a spectral diagnostic system for guiding in brain cancer surgery according to the present invention.
Fig. 3 shows a longitudinal cross-sectional view of an excitation light source and a spectrometer for a guided in brain cancer surgery spectral diagnostic system according to the invention.
Fig. 4 shows an example diagram of a multimode fiber optic probe for a guided in brain cancer surgery spectroscopic diagnostic system according to the present invention.
Fig. 5 shows an exemplary diagram of a control unit interface for a spectral diagnostic system for intraoperative guidance of brain cancer in accordance with the present invention.
Fig. 6 shows a schematic diagram of an intraoperative alert interface for a spectroscopic diagnostic system for intraoperative guidance of brain cancer in accordance with the present invention.
Fig. 7 shows a flow chart of spectral data analysis for a guided in brain cancer surgery spectral diagnostic system according to the present invention.
Fig. 8 shows a schematic diagram of an intraoperative guided brain cancer surgical diagnosis of a spectroscopic diagnostic system for intraoperative guided brain cancer in accordance with the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the term "comprising" and any variations thereof in the description of the invention and the claims and in the above-described figures is intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device comprising a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or device, but may include other steps or elements not expressly listed.
As shown in fig. 1-6, such a spectroscopic diagnostic system for guiding in brain cancer surgery comprises:
the device comprises an excitation light source unit, a spectrum acquisition unit, a control unit, a data processing unit and an intraoperative warning unit;
the control unit controls the laser light source unit and the spectrum acquisition unit to cooperatively work to acquire spectrum data, and the data processing unit and the intraoperative early warning unit are combined to make tumor level early warning for an intraoperative surgeon; the data processing unit is connected with the control unit and the intraoperative warning unit, the control unit inputs the spectrum data into the data processing unit, and the data processing unit outputs the result to the intraoperative warning unit after analyzing the data;
the data processing unit comprises a deep learning development board 5 which calculates weave optical parameters of 3 groups of different light source-detector distances from 3 groups of diffuse reflection spectrum data through inversion operation of Monte Carlo; inputting the 3 groups of tissue optical parameters and the 3 groups of endogenous fluorescence spectrum data with the same light source-detector distance into a fluorescence quantification algorithm to obtain 3 groups of fluorescence quantification data; respectively inputting 3 groups of tissue optical parameters and 3 groups of fluorescence quantitative data into a multi-layer perceptron model to obtain tumor grading prediction, wherein the multi-layer perceptron model can grade a data set into high-grade tumor, low-grade tumor and normal tissue; the data processing unit will obtain 3 sets of tissue optical parameters and 3 sets of predictions of fluorescence quantitative data for each execution.
The invention can combine the diffuse reflection spectrum with the endogenous fluorescence spectrum, detect the tissue optical characteristics and the endogenous fluorescence information of the target focus, and grade the tumor of the target focus by using a machine learning algorithm, thereby being capable of giving out normal and pathological diagnosis results in real time or near real time, and the detection probe can easily reach an operation area to meet the in-vivo diagnosis requirement in operation, and has no damage to brain tissues in-vivo use.
Preferably, the excitation light source unit includes: 3 white light LED light sources 14, 15 and 16 with the wavelength range of 300-1000nm, 3 405nm purple light LED light sources 11, 12 and 13, an LED controller 17 and an excitation light source panel 1; the LED controller 17 is connected with the excitation light source panel 1 through a data line and controls the on-off, input current and excitation time of the LEDs.
Preferably, the spectrum acquisition unit comprises: multimode optical fiber 3, optical fiber probe 38, spectrometer 2; the number of the total channels of the optical fiber probes 38 is 7, the optical fibers are distributed in a straight shape, the core diameter of each optical fiber is 200 mu m, and the center distance between every two adjacent optical fibers is 240 mu m; the first channel 34 is connected with the spectrometer, the second channels 35, 36 and 37 are connected with 3 white light LED light sources 14, 15 and 16 in the excitation light source unit, and the third channels 31, 32 and 33 are connected with 3 405nm purple light LED light sources 11, 12 and 13 in the excitation light source unit; 3 diffuse reflection spectrum data and endogenous fluorescence spectrum data with different light source-detector distances are acquired each time, and the data with different light source-detector distances have tissue optical information and endogenous fluorescence information with different depths of a target focus.
Preferably, the control unit includes: a computer 4, a USB controller 41 and a data acquisition unit 42 based on a LabVIEW system; the computer 4 is connected with the LED controller 17 and the spectrometer 2 by using the USB controller 41, and the data acquisition unit 42 based on the LabVIEW system is used for controlling the switch and system parameters of the system, wherein the system parameters comprise: the excitation sequence of the light source, the excitation time of the light source, the integration time of the spectrometer.
Preferably, the intraoperative warning unit includes: a display 6, a display interface 60 and a warning buzzer 63. The content of the display interface comprises: tumor level prediction graph 61, warning lamp 62, diffuse reflectance spectrum graph 64, fluorescence spectrum graph 65; wherein,
when more than one group of prediction results are high-level tumors, the prediction target focus is the high-level tumor, the warning buzzer 63 sounds high frequency to warn the intraoperative surgeon, the arrow in the tumor early warning diagram 61 points to the high-level tumor area, and the high-level tumor warning lamp in the warning lamp 62 is lighted;
when more than one of the prediction results is low-level tumor, the prediction target focus is low-level tumor, the warning buzzer 63 sounds high-frequency warning to warn the surgeon in the operation, the arrow in the tumor warning diagram 61 points to the low-level tumor area, and the low-level tumor warning lamp in the warning lamp 62 is lighted;
when the predicted target lesion is normal tissue, an arrow in the tumor early warning diagram 61 points to a normal tissue area, and a normal tissue warning lamp in the warning lamp 62 is lighted.
As shown in fig. 7, there is also provided a method of operating a spectroscopic diagnostic system for guiding in brain cancer surgery, comprising the steps of:
(1) And (3) data acquisition: the multimode optical fiber is connected with the spectrometer and the excitation light source, and the spectrometer and the excitation light source are connected with the computer by using the USB controller. Aiming the multimode fiber probe at a target focus, and acquiring diffuse reflection spectrum and endogenous fluorescence spectrum by using data acquisition software based on a LabVIEW system;
(2) Calculating optical parameters and fluorescence quantitative data of the tissue: extracting tissue optical parameters of a target focus by using the diffuse reflection spectrum data obtained in the step (1), and performing fluorescence quantitative operation by using the spectrum data of the endogenous fluorescence obtained in the step (1) to obtain fluorescence quantitative data;
(3) Training a machine learning model: using the tissue optical parameters and fluorescence quantitative data obtained in the step (2) as a data set, inputting the data set into a machine learning model for model training, determining an optimal threshold value of a classification model, and dividing a target focus into the following three levels: normal tissue, low grade tumor, high grade tumor;
(4) The system gives a warning prompt to the clinical surgeon in operation according to the grade of the target focus obtained in the step (3).
Specific embodiments of the present invention are described in detail below.
Example 1: diagnosis of brain tissue as high grade tumor
As shown in fig. 8, the workflow of the present invention is:
(1) The multimode optical fiber 3 is connected with the excitation light source 1 and the spectrometer 2, the computer 4 is connected with the excitation light source 1 and the spectrometer 2 by using the USB controller 41, the excitation light source 1 and the spectrometer 2 are controlled to work cooperatively, and the computer 4, the deep learning development board 5 and the display 6 are connected.
(2) Attaching the multimode fiber optic probe 38 to the lesion 7 to be tested;
(3) Setting excitation sequences of excitation light sources as channel01, channel02, channel03, channel04, channel05 and channel06 at a control unit interface (figure 5), setting excitation time as 200ms, setting integration time of a spectrometer as 100ms, and clicking to start acquisition.
(4) In the intraoperative warning unit, the spectrum curves of the diffuse reflection spectrum and the fluorescence spectrum of the brain tissue at the part can be seen. The intra-operative warning unit predicts and classifies the lesion 7 at the site as a high-grade tumor, the tumor grade prediction graphic 61 points to a high-grade tumor area, a high-grade tumor warning lamp in the warning lamp 62 is lighted, and the warning buzzer 63 sounds high-frequency to warn the surgeon.
Example 2: diagnosing brain tissue as normal tissue
As shown in fig. 8, the workflow of the present invention is:
(1) The multimode optical fiber 3 is connected with the excitation light source 1 and the spectrometer 2, the computer 4 is connected with the excitation light source 1 and the spectrometer 2 by using the USB controller 41, the excitation light source 1 and the spectrometer 2 are controlled to work cooperatively, and the computer 4, the deep learning development board 5 and the display 6 are connected.
(2) Attaching the multimode fiber optic probe 38 to the lesion 7 to be tested;
(3) Setting excitation sequences of excitation light sources as channel01, channel02, channel03, channel04, channel05 and channel06 at a control unit interface (figure 5), setting excitation time as 200ms, setting integration time of a spectrometer as 100ms, and clicking to start acquisition.
(4) In the intraoperative warning unit, the spectrum curves of the diffuse reflection spectrum and the fluorescence spectrum of the brain tissue at the part can be seen. The intra-operative warning unit predicts and classifies the lesion 7 at the site as normal tissue, the tumor level prediction graphic 61 points to the normal tissue area, and the normal tissue warning lamp in the warning lamp 62 is lighted.
The present invention is not limited to the preferred embodiments, but can be modified in any way according to the technical principles of the present invention, and all such modifications, equivalent variations and modifications are included in the scope of the present invention.
Claims (3)
1. A spectroscopic diagnostic system for guidance in brain cancer surgery, characterized by: it comprises the following steps:
the device comprises an excitation light source unit, a spectrum acquisition unit, a control unit, a data processing unit and an intraoperative warning unit;
the control unit controls the laser light source unit and the spectrum acquisition unit to cooperatively work to acquire spectrum data, and the data processing unit and the intraoperative early warning unit are combined to make tumor level early warning for an intraoperative surgeon; the data processing unit is connected with the control unit and the intraoperative warning unit, the control unit inputs the spectrum data into the data processing unit, and the data processing unit outputs the result to the intraoperative warning unit after analyzing the data;
the data processing unit comprises a deep learning development board (5) which calculates tissue optical parameters of 3 groups of different light source-detector distances from 3 groups of diffuse reflection spectrum data through inversion operation of Monte Carlo; inputting 3 groups of tissue optical parameters and 3 groups of endogenous fluorescence spectrum data with different light source-detector distances into a fluorescence quantification algorithm to obtain 3 groups of fluorescence quantification data; respectively inputting 3 groups of tissue optical parameters and 3 groups of fluorescence quantitative data into a multi-layer perceptron model to obtain tumor grading prediction, wherein the multi-layer perceptron model can grade a data set into high-grade tumor, low-grade tumor and normal tissue; the data processing unit obtains 3 groups of tissue optical parameters and 3 groups of predicted results of fluorescence quantitative data after each execution;
the excitation light source unit includes: 3 white light LED light sources (14, 15, 16) with the wavelength range of 300-1000nm, 3 405nm purple light LED light sources (11, 12, 13), an LED controller (17) and an excitation light source panel (1); the LED controller (17) is connected with the excitation light source panel (1) through a data line and controls the on-off, input current and excitation time of the LEDs; the spectrum acquisition unit comprises: multimode optical fiber (3), optical fiber probe (38), spectrometer (2); the total number of channels of the optical fiber probes (38) is 7, the optical fibers are distributed in a straight shape, the core diameter of each optical fiber is 200 mu m, and the center distance between every two adjacent optical fibers is 240 mu m; wherein the first channel (34) is connected with the spectrometer, the second channels (35, 36, 37) are connected with 3 white light LED light sources (14, 15, 16) in the excitation light source unit, and the third channels (31, 32, 33) are connected with 3 405nm purple light LED light sources (11, 12, 13) in the excitation light source unit; 3 diffuse reflection spectrum data and endogenous fluorescence spectrum data with different light source-detector distances are acquired each time, and the data with different light source-detector distances have tissue optical information and endogenous fluorescence information with different depths of a target focus.
2. The spectroscopic diagnostic system for in-brain cancer surgery guidance according to claim 1, wherein: the control unit includes: a computer (4), a USB controller (41) and a data acquisition unit (42) based on a LabView system; a computer (4) is connected with an LED controller (17) and a spectrometer (2) by using a USB controller (41), and a data acquisition unit (42) based on a LabView system is used for controlling the switch and system parameters of the system, wherein the system parameters comprise: the excitation sequence of the light source, the excitation time of the light source, the integration time of the spectrometer.
3. The spectroscopic diagnostic system for in-brain cancer guidance as claimed in claim 2, wherein: the intraoperative warning unit comprises: display (6), display interface (60), warning bee calling organ (63), the content of display interface includes: tumor level prediction graph (61), warning lamp (62), diffuse reflection spectrum graph (64) and fluorescence spectrum graph (65); wherein,
when more than one group of prediction results are high-level tumors, the prediction target focus is the high-level tumor, the warning buzzer (63) sounds high frequency to warn the surgeon in the operation, the arrow in the tumor early warning diagram (61) points to the high-level tumor area, and the high-level tumor warning lamp in the warning lamp (62) is lighted;
when more than one of the prediction results is low-level tumor, the prediction target focus is low-level tumor, the warning buzzer (63) sounds high-frequency warning to warn the surgeon in the operation, the arrow in the tumor warning diagram (61) points to the low-level tumor area, and the low-level tumor warning lamp in the warning lamp (62) is lighted;
when the predicted target focus is normal tissue, an arrow in the tumor early warning diagram (61) points to a normal tissue area, and a normal tissue warning lamp in the warning lamp (62) is lighted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210140944.3A CN114366037B (en) | 2022-02-16 | 2022-02-16 | Spectral diagnosis system for guiding in brain cancer operation and working method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210140944.3A CN114366037B (en) | 2022-02-16 | 2022-02-16 | Spectral diagnosis system for guiding in brain cancer operation and working method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114366037A CN114366037A (en) | 2022-04-19 |
| CN114366037B true CN114366037B (en) | 2023-11-24 |
Family
ID=81146563
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210140944.3A Active CN114366037B (en) | 2022-02-16 | 2022-02-16 | Spectral diagnosis system for guiding in brain cancer operation and working method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114366037B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6377841B1 (en) * | 2000-03-31 | 2002-04-23 | Vanderbilt University | Tumor demarcation using optical spectroscopy |
| CN1474175A (en) * | 2002-04-15 | 2004-02-11 | 鹰 黄 | Super fine optical spectram imaging instrument or system |
| CN1890557A (en) * | 2003-11-28 | 2007-01-03 | Bc肿瘤研究所 | Multimodal detection of tissue abnormalities based on raman and background fluorescence spectroscopy |
| CN101137322A (en) * | 2005-01-21 | 2008-03-05 | 博世创医疗公司 | Method and apparatus for measuring cancerous changes from reflectance spectral measurements obtained during endoscopic imaging |
| CN103163111A (en) * | 2013-02-25 | 2013-06-19 | 天津大学 | Early stage cervical carcinoma detection system integrating fluorescent mesoscope imaging and optical coherence tomography (OCT) |
| CN110470646A (en) * | 2019-08-23 | 2019-11-19 | 成都大象分形智能科技有限公司 | Tumor tissues identifying system based on artificial intelligence and Raman spectrum |
| CN110897593A (en) * | 2019-10-24 | 2020-03-24 | 南京航空航天大学 | Cervical cancer pre-lesion diagnosis method based on spectral characteristic parameters |
| CN111398250A (en) * | 2020-03-02 | 2020-07-10 | 北京理工大学 | Tumor diagnosis method based on molecular fragment spectrum generated by interaction of light and substance |
| CN111624191A (en) * | 2020-03-02 | 2020-09-04 | 北京理工大学 | Off-body universal brain tumor biopsy and boundary determining device |
| CN113648547A (en) * | 2021-09-01 | 2021-11-16 | 北京理工大学 | Photodynamic accurate diagnosis and treatment device under guidance of multimode image and working method thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2529202B1 (en) * | 2010-01-25 | 2022-09-14 | University Health Network | Device, system and method for quantifying fluorescence and optical properties |
-
2022
- 2022-02-16 CN CN202210140944.3A patent/CN114366037B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6377841B1 (en) * | 2000-03-31 | 2002-04-23 | Vanderbilt University | Tumor demarcation using optical spectroscopy |
| CN1474175A (en) * | 2002-04-15 | 2004-02-11 | 鹰 黄 | Super fine optical spectram imaging instrument or system |
| CN1890557A (en) * | 2003-11-28 | 2007-01-03 | Bc肿瘤研究所 | Multimodal detection of tissue abnormalities based on raman and background fluorescence spectroscopy |
| CN101137322A (en) * | 2005-01-21 | 2008-03-05 | 博世创医疗公司 | Method and apparatus for measuring cancerous changes from reflectance spectral measurements obtained during endoscopic imaging |
| CN103163111A (en) * | 2013-02-25 | 2013-06-19 | 天津大学 | Early stage cervical carcinoma detection system integrating fluorescent mesoscope imaging and optical coherence tomography (OCT) |
| CN110470646A (en) * | 2019-08-23 | 2019-11-19 | 成都大象分形智能科技有限公司 | Tumor tissues identifying system based on artificial intelligence and Raman spectrum |
| CN110897593A (en) * | 2019-10-24 | 2020-03-24 | 南京航空航天大学 | Cervical cancer pre-lesion diagnosis method based on spectral characteristic parameters |
| CN111398250A (en) * | 2020-03-02 | 2020-07-10 | 北京理工大学 | Tumor diagnosis method based on molecular fragment spectrum generated by interaction of light and substance |
| CN111624191A (en) * | 2020-03-02 | 2020-09-04 | 北京理工大学 | Off-body universal brain tumor biopsy and boundary determining device |
| CN113648547A (en) * | 2021-09-01 | 2021-11-16 | 北京理工大学 | Photodynamic accurate diagnosis and treatment device under guidance of multimode image and working method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 漫反射光谱在体检测裸鼠胶质瘤;傅楚华;陈图南;李钊;王海峰;黄立贤;谭亮;张大勇;冯华;李飞;;中国微侵袭神经外科杂志(第09期);第415-418页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114366037A (en) | 2022-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4602765B2 (en) | Near-infrared spectroscopic analysis of blood vessel walls | |
| US8649849B2 (en) | Optical methods to intraoperatively detect positive prostate and kidney cancer margins | |
| US6594518B1 (en) | Device and method for classification of tissue | |
| JP2005534428A5 (en) | ||
| US20230148874A1 (en) | Device for determining information relating to a suspected occluding object | |
| EP2814375B1 (en) | Photonic probe apparatus with integrated tissue marking facility | |
| EP3128917A1 (en) | System and method for assessing a cancer status of biological tissue | |
| EP1891893A1 (en) | Disease judging supporting system | |
| EP1852101B1 (en) | Biophoton measuring instrument | |
| JP2017534321A (en) | System and method using composite optical probe | |
| CN109781701A (en) | Real-time detection method in a kind of parathyroidectomy based on Raman spectroscopy | |
| JP6282013B2 (en) | Tumor tissue classification system using personalized threshold | |
| EP2837335A1 (en) | Method for nerve detection by raman scattering spectroscopy | |
| CN114366037B (en) | Spectral diagnosis system for guiding in brain cancer operation and working method thereof | |
| CA2815546A1 (en) | System and method for noninvasive tissue examination | |
| US10531862B2 (en) | System and method for attaching a biopsy collecting device to a spectroscopy system | |
| RU183278U1 (en) | DEVICE FOR NEUROSURGICAL ASPIRATION WITH SPECTROSCOPIC AND ELECTROPHYSIOLOGICAL CONTROL | |
| CA2501613A1 (en) | Method and device for the non-invasive analysis of metabolic processes | |
| US9913628B2 (en) | System and method for attaching a biopsy collecting device to a spectroscopy system | |
| Maintz et al. | Auditory and visual user interface for optical guidance during stereotactic brain tumor biopsies | |
| RU2676647C1 (en) | Method for determining type of biological tissue | |
| EP2642910A1 (en) | Device and method for determining a disease activity | |
| Rossberg et al. | Explainable machine learning for the multiclass classification of diffuse reflectance spectroscopy signals in orthopaedic applications | |
| Anbunesan et al. | Fluorescence Lifetime Imaging Distinguishes Glioblastoma Multiforme Infiltrative Edges in Patients | |
| Hutchings et al. | Ultra-low spatial resolution Raman mapping using a novel fibre optic probe |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |