CN115619737A - Biological tissue welding effect detection device - Google Patents
Biological tissue welding effect detection device Download PDFInfo
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- CN115619737A CN115619737A CN202211270024.XA CN202211270024A CN115619737A CN 115619737 A CN115619737 A CN 115619737A CN 202211270024 A CN202211270024 A CN 202211270024A CN 115619737 A CN115619737 A CN 115619737A
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- 238000003466 welding Methods 0.000 title claims abstract description 36
- 230000000694 effects Effects 0.000 title claims abstract description 24
- 238000001514 detection method Methods 0.000 title claims abstract description 10
- 238000003384 imaging method Methods 0.000 claims abstract description 28
- 238000011156 evaluation Methods 0.000 claims abstract description 23
- 238000013135 deep learning Methods 0.000 claims abstract description 13
- 230000010287 polarization Effects 0.000 claims abstract description 6
- 230000003287 optical effect Effects 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 6
- 238000000034 method Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 6
- 206010052428 Wound Diseases 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000000170 cell membrane Anatomy 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000015271 coagulation Effects 0.000 description 1
- 238000005345 coagulation Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000004043 dyeing Methods 0.000 description 1
- 230000035876 healing Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
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- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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Abstract
The invention discloses a biological tissue welding effect detection device, and relates to the technical field of biological welding. The system comprises an OCT imaging module, a deep learning module, a super-resolution reconstruction module, a denoising module and an evaluation module, wherein the deep learning module is electrically connected with the OCT imaging module, the resolution reconstruction module is electrically connected with the OCT imaging module, the denoising module is electrically connected with the OCT imaging module, the OCT imaging module is electrically connected with the evaluation module, the OCT imaging module comprises a computer, a reflecting mirror, a vibrating mirror, an objective lens, a beam expander, a polarization controller, a charge coupler and an interferometer, the effect of automatically evaluating the welding effect of the biological tissue is achieved conveniently in use, compared with the traditional direct observation mode, the system has the advantages that the experience requirement on an operator is lower, the probability of error occurrence is lower, the biological tissue does not need to be sliced, and the detection process is quicker and more convenient.
Description
Technical Field
The invention relates to the technical field of biological welding, in particular to a biological tissue welding effect detection device.
Background
The principle of biological tissue welding is to promote the coagulation of protein molecules through the action of heat energy. During operation, two-electrode welding tongs are used, high-frequency high-voltage current is used to destroy cell membrane to decompose condensed liquid, and then tissue at wound is pressed to complete the welding process. Generally, after about one month, the morphological structure of the biological tissue can be completely restored as before, and the surgical site is hardly found. Compared with the traditional suturing technology, the biological tissue welding instrument adopted clinically can greatly shorten the suturing time and reduce the blood loss and blood transfusion of patients, thereby reducing the possibility of complications and the operation cost.
After welding of biological tissues is completed, welding effect needs to be monitored, the traditional judgment by observing the surface has high requirements on experience of operators, and errors may exist, for example, the surface welding is successful, but the welding effect is not good in depth, and the judgment error may influence the healing of wounds; although the biological tissue section detection mode is accurate, the whole process is material-taking, the time consumption for dyeing, photographing and observing is long, and the detection of the welding effect cannot be rapidly carried out.
Therefore, a biological tissue welding effect detection device is provided.
Disclosure of Invention
The invention aims to: in order to solve the problems mentioned in the background art, the invention provides a device for detecting the welding effect of biological tissues.
The invention specifically adopts the following technical scheme for realizing the purpose:
the device for detecting the welding effect of the biological tissue comprises an OCT imaging module, a deep learning module, a super-resolution reconstruction module, a denoising module and an evaluation module, wherein the deep learning module is electrically connected with the OCT imaging module, the resolution reconstruction module is electrically connected with the OCT imaging module, the denoising module is electrically connected with the OCT imaging module, and the OCT imaging module is electrically connected with the evaluation module.
Further, the OCT imaging module comprises a computer, a reflecting mirror, a galvanometer, an objective lens, a beam expander, a polarization controller, a charge coupler and an interferometer.
Further, the interferometer includes an optical isolator, an optical coupler, and an optical collimator.
Further, the super-resolution reconstruction module comprises a generator, a tag image comparison module, a discriminator and a countermeasure network, wherein a tag image is stored in the tag image comparison module.
Further, the denoising module comprises an encoder, an encoding feature extraction module and a decoder, wherein the encoder comprises a content encoder and a noise encoder, and the encoding feature extraction module comprises a content encoding feature and a noise encoding feature.
Further, the evaluation module comprises a feature database, a comparison module and an evaluation output module.
Furthermore, the characteristic database stores a healthy biological tissue characteristic image, and the evaluation output module is electrically connected with a printer.
The invention has the following beneficial effects:
according to the invention, the OCT imaging module is used for acquiring the image of the welding position of the biological tissue, the deep learning module is used for identifying the visual pattern from the pixel image, the pattern with variability can be identified, the denoising module is used for denoising the acquired visual pattern, the super-resolution reconstruction module is used for reconstructing the pattern with low resolution into the clear pattern with high resolution, the evaluation module is used for comparing and analyzing the pattern of the welding position of the device, and the welding effect evaluation is output.
Drawings
FIG. 1 is a schematic view of the structure of the present invention;
FIG. 2 is a schematic diagram of an OCT imaging module of the invention;
FIG. 3 is a schematic diagram of an interferometer of the present invention;
FIG. 4 is a schematic diagram of the deep learning module workflow of the present invention;
FIG. 5 is a schematic diagram of the super-resolution reconstruction module of the present invention;
FIG. 6 is a schematic diagram of a denoising module according to the present invention;
FIG. 7 is a schematic diagram of an evaluation module according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," and the like are used solely to distinguish one from another, and are not to be construed as indicating or implying relative importance.
The electrical components presented in the document are all electrically connected with an external master controller and 220V mains, and the master controller can be a conventional known device controlled by a computer or the like.
In the description of the embodiments of the present invention, it should be noted that the terms "inside", "outside", "upper", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or orientations or positional relationships conventionally arranged when products of the present invention are used, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements indicated must have specific orientations, be constructed in specific orientations, and operated, and thus, cannot be construed as limiting the present invention.
As shown in fig. 1, a device for detecting welding effect of biological tissue includes an OCT imaging module, a deep learning module, a super-resolution reconstruction module, a denoising module, and an evaluation module, where the deep learning module is electrically connected to the OCT imaging module, the resolution reconstruction module is electrically connected to the OCT imaging module, the denoising module is electrically connected to the OCT imaging module, and the OCT imaging module is electrically connected to the evaluation module, and more specifically, the OCT imaging module performs image acquisition on a welding position of biological tissue, and identifies a visual pattern from a pixel image through the deep learning module, and can identify a pattern with variability, the deep learning module mainly uses a convolution kernel to slide on an input image or a feature map according to a certain step length to calculate a weighted sum of elements on a coverage area of the convolution kernel, so as to update an element value of an image or a corresponding position in a central position of the convolution kernel, traverse a complete image or feature map, obtain a new feature map, perform denoising processing on the visual pattern through the denoising module, reconstruct a pattern with low resolution into a clear pattern with high resolution through the super-resolution reconstruction module, compare and analyze the welding position, and evaluate the welding effect.
As shown in fig. 2, the OCT imaging module includes a computer, a mirror, a galvanometer, an objective lens, a beam expander, a polarization controller, a charge coupler, and an interferometer, and more specifically, light passing through the light coupler is divided into two parts: wherein, a part of light passes through a galvanometer, and passes through an objective lens with ten times magnification to scan the sample in the x-axis direction and the y-axis direction, and scattered light generated by the sample returns to the light coupler; the other part of the light passes through the polarization controller and the optical attenuator, passes through the objective lens with ten times of magnification and reaches the reflector at the reference end, and the reflected light of the reflector returns to the light coupler and the scattered light at the sample end which returns to the light coupler is interfered. Then the light beam passes through a polarization controller and a beam expander, and then the light beam is subjected to grating to generate dispersion. And finally, the interference signal is amplified and filtered, then is transmitted to a charge coupler, and is transmitted to a computer for reconstruction through digital conversion, so that a two-dimensional image of the measured sample is obtained.
As shown in fig. 3, the interferometer includes an optical isolator, an optical coupler, and an optical collimator.
As shown in fig. 5, the super-resolution reconstruction module includes a generator, a tag image comparison module, a discriminator, and a countermeasure network, where the tag image is stored in the tag image comparison module, and it should be noted that the super-resolution reconstruction module performs OCT image super-resolution reconstruction by using the generated countermeasure network, with a noisy low-resolution image as an input and a clear high-resolution OCT image as a tag. The low-resolution input image with noise is obtained by down-sampling the original acquired OCT image, and the clear high-resolution label image is the result of the registration average corresponding to the original OCT image obtained by using commercial equipment. The purpose of simultaneous denoising and super-resolution reconstruction of the OCT image is better achieved by generating a countermeasure network, and the discriminator is mainly used for distinguishing which images are generated by the network and which are label images from all clear high-resolution images, so that the generator is prompted to generate a more vivid target image.
As shown in fig. 6, the denoising module includes an encoder, an encoding feature extraction module and a decoder, the encoder includes a content encoder and a noise encoder, the encoding feature extraction module includes a content encoding feature and a noise encoding feature, and more specifically, the clear image can only be converted into a content space by decoupling an image of a noise domain into a specific content space and a fixed noise space, the image sample is decomposed from the image domain into a corresponding content space or noise space by the encoder, and the encoding feature in the content space and the noise space is mapped back to the image domain by the generator.
As shown in fig. 7, the evaluation module includes a feature database, a comparison module, and an evaluation output module.
As shown in fig. 7, the characteristic database stores a healthy biological tissue characteristic image, and the evaluation output module is electrically connected to a printer. More specifically, the obtained high-resolution clear image is compared with the image in the characteristic database through a comparison module so as to give a biological tissue welding effect rating, and the rating is output through an evaluation output module and can be printed through a printer.
In summary, the following steps: according to the invention, the OCT imaging module is used for acquiring images of the welding position of the biological tissue, the deep learning module is used for identifying the visual pattern from the pixel image and identifying the pattern with variability, the denoising module is used for denoising the acquired visual pattern, the super-resolution reconstruction module is used for reconstructing the pattern with low resolution into the clear pattern with high resolution, the evaluation module is used for comparing and analyzing the pattern of the welding position of the device and outputting the welding effect evaluation, so that the effect of automatically evaluating the welding effect of the biological tissue is realized in use, compared with the traditional direct observation mode, the experience requirement on an operator is lower, the error probability is lower, the slicing processing of the biological tissue is not needed, and the detection process is quicker and more convenient.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The biological tissue welding effect detection device is characterized by comprising an OCT imaging module, a deep learning module, a super-resolution reconstruction module, a de-noising module and an evaluation module, wherein the deep learning module is electrically connected with the OCT imaging module, the resolution reconstruction module is electrically connected with the OCT imaging module, the de-noising module is electrically connected with the OCT imaging module, and the OCT imaging module is electrically connected with the evaluation module.
2. The apparatus of claim 1, wherein the OCT imaging module comprises a computer, a mirror, a galvanometer, an objective lens, a beam expander, a polarization controller, a charge-coupled device, and an interferometer.
3. The apparatus as claimed in claim 2, wherein the interferometer comprises an optical isolator, an optical coupler and an optical collimator.
4. The device for detecting the welding effect of biological tissues, according to claim 1, wherein the super-resolution reconstruction module comprises a generator, a tag image comparison module, a discriminator and a countermeasure network, and the tag image comparison module stores a tag image therein.
5. The device for detecting welding effect of biological tissues as claimed in claim 1, wherein the de-noising module comprises an encoder, an encoding feature extraction and a decoder, the encoder comprises a content encoder and a noise encoder, and the encoding feature extraction comprises a content encoding feature and a noise encoding feature.
6. The device for detecting the welding effect of biological tissues, according to claim 1, wherein the evaluation module comprises a feature database, a comparison module and an evaluation output module.
7. The device for detecting the welding effect of biological tissues according to claim 1, wherein the characteristic database stores characteristic images of healthy biological tissues, and the evaluation output module is electrically connected with a printer.
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| CN202211270024.XA CN115619737A (en) | 2022-10-18 | 2022-10-18 | Biological tissue welding effect detection device |
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| CN202211270024.XA CN115619737A (en) | 2022-10-18 | 2022-10-18 | Biological tissue welding effect detection device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117689760A (en) * | 2024-02-02 | 2024-03-12 | 山东大学 | OCT axial super-resolution method and system based on histogram information network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117689760A (en) * | 2024-02-02 | 2024-03-12 | 山东大学 | OCT axial super-resolution method and system based on histogram information network |
| CN117689760B (en) * | 2024-02-02 | 2024-05-03 | 山东大学 | OCT axial super-resolution method and system based on histogram information network |
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