CN112505911A - Lens-free self-adaptive microscopic imaging device based on deep learning - Google Patents
Lens-free self-adaptive microscopic imaging device based on deep learning Download PDFInfo
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- CN112505911A CN112505911A CN202011273174.7A CN202011273174A CN112505911A CN 112505911 A CN112505911 A CN 112505911A CN 202011273174 A CN202011273174 A CN 202011273174A CN 112505911 A CN112505911 A CN 112505911A
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- 238000003384 imaging method Methods 0.000 title claims abstract description 57
- 238000013135 deep learning Methods 0.000 title claims abstract description 26
- 238000006073 displacement reaction Methods 0.000 claims abstract description 23
- 230000003044 adaptive effect Effects 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 230000005540 biological transmission Effects 0.000 claims description 14
- 239000000956 alloy Substances 0.000 claims description 6
- 229910045601 alloy Inorganic materials 0.000 claims description 3
- 238000000386 microscopy Methods 0.000 claims 8
- 239000011521 glass Substances 0.000 claims 1
- 239000007769 metal material Substances 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 9
- 238000013527 convolutional neural network Methods 0.000 abstract description 5
- 230000003287 optical effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000009087 cell motility Effects 0.000 description 1
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B21/00—Microscopes
- G02B21/06—Means for illuminating specimens
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B21/00—Microscopes
- G02B21/24—Base structure
- G02B21/26—Stages; Adjusting means therefor
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B21/00—Microscopes
- G02B21/36—Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B21/00—Microscopes
- G02B21/36—Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
- G02B21/362—Mechanical details, e.g. mountings for the camera or image sensor, housings
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03H—HOLOGRAPHIC PROCESSES OR APPARATUS
- G03H1/00—Holographic processes or apparatus using light, infrared or ultraviolet waves for obtaining holograms or for obtaining an image from them; Details peculiar thereto
- G03H1/0005—Adaptation of holography to specific applications
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03H—HOLOGRAPHIC PROCESSES OR APPARATUS
- G03H1/00—Holographic processes or apparatus using light, infrared or ultraviolet waves for obtaining holograms or for obtaining an image from them; Details peculiar thereto
- G03H1/04—Processes or apparatus for producing holograms
- G03H1/0443—Digital holography, i.e. recording holograms with digital recording means
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03H—HOLOGRAPHIC PROCESSES OR APPARATUS
- G03H1/00—Holographic processes or apparatus using light, infrared or ultraviolet waves for obtaining holograms or for obtaining an image from them; Details peculiar thereto
- G03H1/0005—Adaptation of holography to specific applications
- G03H2001/005—Adaptation of holography to specific applications in microscopy, e.g. digital holographic microscope [DHM]
Abstract
The invention relates to a lens-free self-adaptive microscopic imaging device based on deep learning, and particularly relates to the technical field of microscopic imaging. The invention relates to a lens-free self-adaptive microscopic imaging device for deep learning, which comprises a main body, an LED light source, an iris diaphragm, a sample stage, a self-adaptive displacement stage, an image sensor and a deep learning calculation platform, wherein the specific working process comprises the following steps: in the pre-imaging stage, light emitted by an LED irradiates a sample on a sample table after passing through a diaphragm, an image sensor collects a holographic image and sends the holographic image to a computing platform, the platform controls an adaptive displacement table to adjust the position of the image sensor according to the quality of the obtained image, the image is recorded again and transmitted to a depth learning computing platform after the image is adjusted to the optimal imaging position, and the holographic image is reconstructed, intelligently identified and calibrated by utilizing a convolutional neural network. The device has the self-adaptation displacement platform, and the imaging process is automatic, and is intelligent, and the imaging speed is fast, the high quality.
Description
Technical Field
The invention relates to the technical field of microscopic imaging, in particular to a lens-free self-adaptive microscopic imaging device based on deep learning.
Background
The optical microscope is widely applied to the imaging system in the fields of life science research and the like, powerfully promotes the development of the fields of life science and the like, and is mainly applied to the observation and research of life phenomena such as cell movement, vesicle fusion, lipid metabolism and the like. But the optical microscope still has low system automation degree and high cost; large field of view and high resolution imaging cannot be considered; the imaging is easily interfered by the aberration of the high-power objective lens.
In recent years, a lensless microscope based on a coaxial holographic imaging technology is developed rapidly, and can realize compact and high-flux imaging. The lensless microscope records a holographic pattern using an image sensor, and then reconstructs an image of the sample from the holographic pattern by a digital holographic reconstruction method. The technology can eliminate the limitation of the lens structure on the imaging resolution and the imaging field of view, and has the advantages of small volume, simple structure, large imaging field of view, low cost and the like compared with the traditional microscope. However, before imaging by many lens-free imaging devices, the sample still needs to be adjusted, automatically, with a low degree of intelligence and a slow imaging speed.
Aiming at the problems, the invention provides a lens-free self-adaptive microscopic imaging device based on deep learning, which can realize automatic, intelligent and rapid imaging on different samples.
Disclosure of Invention
The present invention is directed to provide a lens-free adaptive microscopic imaging apparatus based on deep learning, so as to solve the problems of automation and low degree of intelligence in the imaging process in the prior art, and improve the imaging speed, in order to solve the problems of the conventional optical microscope and the lens-free microscope. In order to achieve the purpose, the invention relates to a lens-free self-adaptive microscopic imaging device based on deep learning, which is realized by the following technical scheme: the invention is realized by a lens-free microscopic imaging device based on deep learning, and the lens-free microscopic imaging device is composed of an LED light source, an iris diaphragm, a sample stage, an image sensor, a self-adaptive displacement stage, a support and a deep learning calculation platform. The method is characterized in that: the base is horizontally placed, the support is fixed on the upper surface of the base, one end of the support is vertically connected with the base, the LED light source is placed at the other end of the support, the light source emitting direction vertically faces the base, the iris diaphragm is arranged on one side, close to the base, of the LED light source, the sample stage is arranged on one side, close to the base, of the iris diaphragm, the image sensor is arranged on one side, close to the base, of the sample stage, the self-adaptive displacement stage is arranged on one side, close to the base, of the image sensor, the self-adaptive displacement stage comprises two stepping motors, an STM32 single chip microcomputer, a sliding rod, a moving platform, a driving belt and a rectangular support, the stepping motors are respectively arranged at end points of the same sides of a first cross rod and a second cross rod, the STM32 single chip microcomputer is arranged between the two stepping motors, close to one side of, the movable platform is arranged on the sliding rod and can slide along the rod, a plane on one side, close to the sample stage, of the movable platform and a plane on one side, close to the base, of the image sensor are fixedly overlapped, the position of the image sensor can be adjusted, the transmission belt is connected with the stepping motor, and the transmission belt fixing device and the slidable support table are arranged.
Optionally, the integral bracket is made of plastic or alloy and is integrally rectangular.
Optionally, the axial distance between the LED light source and the sample stage is 5cm to 10cm, and the axial distance between the image sensor and the sample stage is 1cm to 2 cm.
Optionally, the LED light source is a white LED, and the distance between the LED light source and the iris diaphragm is 1cm to 3 cm.
Optionally, the iris diaphragm is made of alloy materials, is black as a whole, and can be manually adjusted in aperture.
Optionally, the sample stage comprises a metallic slide holder for holding a slide containing the sample.
Optionally, the image sensor is a CMOS or a CCD, the image sensor includes a USB interface, and the image sensor is connected to the depth learning calculation platform by using the USB interface to perform data transmission.
Optionally, the adaptive displacement table comprises a hard plastic driving belt with small teeth.
Optionally, the adaptive displacement table comprises a stepping motor, which can be single-phase, two-phase, three-phase or multi-phase, and the stepping motor is fixedly connected with the transmission belt through a rotating wheel with small teeth.
The invention has the beneficial effects that:
the application provides a no lens self-adaptation microscopic imaging device based on degree of depth study includes: the system comprises an LED light source, an iris diaphragm, a sample stage, an image sensor and a depth learning and calculating platform. The LED light source, the variable diaphragm, the sample stage, the self-adaptive displacement stage, the image sensor and the integral support which are arranged in sequence form an imaging main body and are connected with the depth learning calculation platform; the LED light source is placed at the uppermost part of the whole device, irradiates a sample platform below the LED light source and is captured by an image sensor at the lowermost part of the whole device; the two stepping motors and the support form a self-adaptive displacement table, and the support is embedded with the image sensor and is positioned below the sample table. In the pre-imaging stage, light emitted by an LED light source irradiates a sample on a sample table after passing through a diaphragm, and an image sensor collects a holographic image and sends the holographic image to a computing platform; the platform judges according to the quality of the obtained image, if the image loss exceeds a certain threshold value, the platform sends an instruction to an STM32 single chip microcomputer, and the STM32 single chip microcomputer controls two stepping motors to adjust the position of the image sensor; and after the image is adjusted to the optimal imaging position, recording the image again and transmitting the image to a deep learning calculation platform, and performing quick reconstruction, intelligent identification and calibration on the holographic image by using a convolutional neural network. The device is provided with a self-adaptive displacement table, can realize automation and intellectualization of an imaging process, and does not need manual adjustment; and the method is combined with a convolutional neural network computing platform based on deep learning, so that large-view-field high-resolution imaging can be realized, the imaging speed is high, and the quality is high.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of an imaging subject of a lens-free adaptive micro-imaging device based on deep learning.
Fig. 2 is an overall schematic diagram of a lens-free adaptive micro-imaging device based on deep learning.
Fig. 3 is a schematic diagram of an adaptive displacement stage apparatus.
Icon: 1-an LED light source; 2-untreated light beam; 3-an iris diaphragm; 4-processed beam, spherical wave; 5-a sample stage; 6-an image sensor; 7-a deep learning computing platform; 8-a stepper motor; 9-a stepper motor; 10-a first cross-bar; 11-STM32 single chip microcomputer; 12-a transmission belt; 13-a slidable pallet; 14-a second cross bar; 15-sliding bar.
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.
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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, 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 referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In order to make the implementation of the present invention clearer, the following detailed description is made with reference to the accompanying drawings.
The application provides a no lens self-adaptation micro-imaging device based on degree of depth study comprises LED light source 1, iris diaphragm 3, sample platform 5, image sensor 6, self-adaptation displacement platform, support and degree of depth study computing platform 7. The method is characterized in that: the base is horizontally placed, the support is fixed on the upper surface of the base, one end of the support is vertically connected with the base, the LED light source 1 is placed at the other end of the support, the light source emitting direction vertically faces the base, the iris diaphragm 3 is arranged on one side, close to the base, of the LED light source 1, the sample stage 5 is arranged on one side, close to the base, of the iris diaphragm 3, the image sensor 6 is arranged on one side, close to the base, of the sample stage 5, the self-adaptive displacement stage is arranged on one side, close to the base, of the image sensor 6, the self-adaptive displacement stage comprises two stepping motors 8 and 9, an STM32 single chip microcomputer 11, a slidable rod 15, a slidable support table 13, a transmission belt 12 and a rectangular support, the stepping motors 8 and 9 are respectively arranged at end points of a first cross rod 10 and a second cross rod 14, the STM32, 9, and is close to first horizontal pole 10 one side between, and links to each other with computing platform 7, accepts platform instruction control step motor 8, 9 adjustment image sensor 6 position, slidable rod 15 is placed between perpendicular to first horizontal pole 10 and second horizontal pole 14, and can follow horizontal pole 10, 14 and slide, slidable saddle 13 sets up on slidable rod 15, slidable saddle 13 can follow pole 15 and slide, and is fixed mutually with image sensor 6, adjustable image sensor 6 position, step motor 8, 9, drive belt fixing device and slidable saddle 13 are connected to drive belt 12.
Optionally, the integral bracket is made of plastic or alloy and is integrally rectangular.
Optionally, the axial distance between the LED light source 1 and the sample stage is 5cm to 10cm, and the axial distance between the image sensor 6 and the sample stage is 1cm to 2 cm.
Optionally, the LED light source 1 is a common LED light source, and the axial distance between the iris diaphragm 3 and the LED light source 1, which is fixed below the common LED light source, is 1cm to 3 cm.
Optionally, the iris diaphragm 3 is made of alloy materials, is black as a whole, and can be manually adjusted in aperture.
Optionally, the sample stage 5 comprises a metallic slide holder for holding a slide containing a sample.
Optionally, a CMOS or CCD camera may be used as the image sensor 6 to record the hologram, and the image sensor 6 includes a USB interface, and the image sensor is connected to the depth learning calculation platform 7 by using the USB interface to perform data transmission.
Optionally, the belt 12 included in the adaptive displacement table is a hard plastic belt with small teeth.
Optionally, the adaptive displacement table comprises stepping motors 8 and 9, which may be single-phase, two-phase, three-phase or multi-phase, and the stepping motors 8 and 9 are fixedly connected to the driving belt 12 through a rotating wheel with small teeth.
The application relates to a no lens self-adaptation microscopic imaging device based on degree of depth study, adopt coaxial holographic imaging mode, during the formation of image, become divergent spherical wave 4 behind iris diaphragm 3 light beam 2 of LED light source 1 transmission, the spherical wave shines on sample platform 5, the hologram is recorded by image sensor 6, on USB connects and spreads into degree of depth study computing platform 7, degree of depth study computing platform 7 utilized the convolutional neural network based on degree of depth study to carry out quick reconstruction and intelligent recognition and demarcation to the hologram. In the pre-imaging stage, the depth learning platform 7 judges according to the image quality, and if the image loss exceeds a threshold value, the self-adaptive displacement table is controlled to adjust the position of the image sensor 6 so as to achieve the optimal imaging position. The imaging process does not need manual adjustment, and the automation and intelligent imaging are completely realized; and the hologram is processed by combining with a convolutional neural network based on deep learning, so that the imaging speed is high and the quality is high.
Claims (9)
1. The utility model provides a no lens self-adaptation microscopic imaging device based on degree of depth study comprises LED light source, iris diaphragm, sample platform, image sensor, self-adaptation displacement platform, support and degree of depth study computing platform, its characterized in that: the base is horizontally placed, the support is fixed on the upper surface of the base, one end of the support is vertically connected with the base, the LED light source is placed at the other end of the support, the light source emitting direction vertically faces the base, the iris diaphragm is arranged on one side, close to the base, of the LED light source, the sample stage is arranged on one side, close to the base, of the iris diaphragm, the image sensor is arranged on one side, close to the base, of the sample stage, the self-adaptive displacement stage is arranged on one side, close to the base, of the image sensor, the self-adaptive displacement stage comprises two stepping motors, an STM32 single chip microcomputer, a sliding rod, a moving platform, a driving belt and a rectangular support, the stepping motors are respectively arranged at end points of the same sides of a first cross rod and a second cross rod, the STM32 single chip microcomputer is arranged between the two stepping motors, close to one side of, the movable platform is arranged on the sliding rod and can slide along the rod, a plane on one side, close to the sample stage, of the movable platform and a plane on one side, close to the base, of the image sensor are fixedly overlapped, the position of the image sensor can be adjusted, the transmission belt is connected with the stepping motor, and the transmission belt fixing device and the slidable support table are arranged.
2. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the integral bracket is made of plastic or alloy and is integrally rectangular.
3. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the axial distance between the LED light source and the sample table is 5cm-10cm, and the axial distance between the image sensor and the sample table is 1cm-2 cm.
4. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the LED light source is a white light LED, and the distance between the LED light source and the iris diaphragm is 1cm-3 cm.
5. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the iris diaphragm is made of alloy materials, is black as a whole, and can manually adjust the aperture.
6. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the sample table comprises a metal material pressing clamp used for fixing a glass slide containing a sample.
7. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the image sensor is a CMOS or CCD, the image sensor comprises a USB interface, and the image sensor is connected with the deep learning calculation platform by the USB interface for data transmission.
8. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the transmission belt included in the self-adaptive displacement platform is a hard plastic transmission belt with small teeth.
9. The lens-free adaptive microscopy imaging device based on deep learning of claim 1, wherein: the self-adaptive displacement platform comprises a stepping motor, wherein the stepping motor can be selected from single phase, double phase, three phase or multiple phases, and is fixedly connected with a transmission belt through a rotating wheel with small teeth.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110927115A (en) * | 2019-12-09 | 2020-03-27 | 杭州电子科技大学 | Lens-free dual-type fusion target detection device and method based on deep learning |
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- 2020-11-13 CN CN202011273174.7A patent/CN112505911A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110927115A (en) * | 2019-12-09 | 2020-03-27 | 杭州电子科技大学 | Lens-free dual-type fusion target detection device and method based on deep learning |
| CN110927115B (en) * | 2019-12-09 | 2022-05-13 | 杭州电子科技大学 | Lens-free dual-type fusion target detection device and method based on deep learning |
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Application publication date: 20210316 |