CN111175301A - Clear image acquisition method for sheath flow microscopic full-thickness flow channel cells - Google Patents
Clear image acquisition method for sheath flow microscopic full-thickness flow channel cells Download PDFInfo
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Abstract
The invention discloses a clear image acquisition method for a cell of a sheath flow microscopic full-thickness runner, which comprises the steps of obtaining and checking an initial position of an objective lens displacement platform and states of a camera and a flash lamp after power-on reset, emptying cached image data, entering an idle waiting state, driving the objective lens displacement platform to move after receiving an acquisition starting instruction, starting the camera acquisition according to a backlight synchronous control signal, storing an image acquired based on a constraint relation into a data cache region, carrying out parallel preprocessing, carrying out automatic tracking, segmentation extraction and automatic collection on the same cell by using a convolutional neural network deep learning model, screening out a cell image with optimal definition in each collection through a definition algorithm, packaging and uploading, clearing all image data cached after preprocessing until receiving an image acquisition stopping instruction, exiting the image acquisition state, the system full-thickness real-time acquisition and real-time processing method greatly improves the accuracy of cell acquisition and the image definition.
Description
Technical Field
The invention relates to the technical field of in-vitro diagnosis cell morphology analysis equipment, in particular to a clear image acquisition method for a sheath flow microscopic full-thickness flow channel cell.
Background
With the continuous progress of social economy, the living standard and health consciousness of people are continuously improved, the accurate medical call is increasingly increased, and the cell morphology analysis is an indispensable detection and analysis means for realizing accurate medical treatment. In the analysis of body fluid formed components, obtaining a clear cell image is a crucial link, and the clear cell image is an important basis for subsequent identification, analysis and diagnosis. The conventional blood cell morphological analyzer adopts a static microscopic technology to take a focusing picture of the current static cell smear, and the clear cell image is easy to obtain. However, in the currently emerging cell morphology analyzer combining the sheath flow technology, no matter a symmetric columnar sheath flow or flat planar sheath flow mode is adopted, all cells cannot be extruded on a high-power microscope imaging focal plane even at a high sheath flow speed due to the random motion of the cells, and the focusing on a single cell cannot be realized at a high speed in real time.
The conventional method is to increase the depth of field, and there are several methods for increasing the depth of field: 1) the field depth can be increased to a certain extent by changing a low power objective lens and increasing the pixel size of an image camera, but the optical magnification and the resolution can be sacrificed; 2) the depth of field is extended through a wavefront coding mode, the method is used abroad more, but the phase template is difficult to process, high in precision requirement and high in cost, meanwhile, the processing precision can cause aberration of different degrees, and the actual quality of an image obtained after decoding cannot be estimated; 3) the method needs to redesign an objective lens, has high processing difficulty, has high requirement on a split-beam image fusion algorithm, and has the depth of field of fusion only about 2 times of that of single-cylinder imaging. Therefore, in the flow cell formation analyzer, how to obtain clear images of cells is a technical problem which troubles workers in research and development.
Disclosure of Invention
The invention aims to provide a method for collecting clear images of cells in a sheath flow microscopic full-thickness flow channel, so as to improve the accuracy and the image definition of cell collection.
In order to achieve the aim, the invention provides a clear image acquisition method for a sheath flow microscopic full-thickness flow channel cell, which comprises the following steps:
acquiring and checking an initial state, clearing a cache image and monitoring a start acquisition instruction;
receiving the acquisition starting instruction, driving the objective lens displacement platform to move, and starting the camera to acquire according to the backlight synchronous control signal;
storing the image data acquired based on the constraint relation into a data cache region, and continuously acquiring images in real time;
monitoring the image data in the data cache region, reading and caching the image data and preprocessing the image data;
carrying out cell tracking, segmentation and collection on the preprocessed image data by using a deep learning model;
and screening, packaging and uploading the collected cell image data by using an image definition algorithm until an acquisition stopping instruction is received, and returning to a waiting acquisition state.
Wherein, the obtaining and checking initial state, clearing the cache image and monitoring the start acquisition instruction comprises:
after power-on reset, the initial position of the objective lens displacement platform and the states of the camera and the flash lamp are obtained, after the states are detected normally, the initial position of the objective lens is verified, cached image data are emptied, and an acquisition starting instruction is monitored in real time.
Wherein, receive the start acquisition instruction, drive objective displacement platform motion to according to backlight synchronous control signal start camera collection, include:
and after receiving the acquisition starting instruction, controlling an image acquisition control card to drive the objective lens displacement platform to reciprocate according to the set frequency and stroke, simultaneously outputting a backlight synchronous control signal, and starting a camera to acquire images.
Wherein, the image data based on the constraint relation is stored in the data buffer area, and the image acquisition is continuously carried out in real time, which comprises:
according to the constraint relation among the imaging area flow channel sheath flow velocity, the imaging area flow channel cell layer thickness, the optical depth of field, the camera CCD width, the camera CCD pixel size width, the camera acquisition frame rate, the sampling times and the objective lens displacement platform, receiving data frames of image data acquired based on the constraint relation, writing the data frames into a cache area until the acquisition is finished, and returning to a power-on reset state.
Monitoring the image data in the data cache region, reading and caching the image data and preprocessing the image data, wherein the method comprises the following steps:
and after receiving the acquisition starting instruction, entering a continuous image processing state, monitoring data in a data cache region in real time, reading the cached image data if the image data is not processed, and performing filtering, noise reduction and image enhancement processing on the image data.
The method for performing cell tracking, segmentation and collection on the preprocessed image data by using the deep learning model comprises the following steps:
and automatically tracking the same cell in the preprocessed image by using a convolutional neural network deep learning model, segmenting and extracting all tracked cell images, automatically collecting, and carrying out secondary constraint verification on coordinates and displacement of an output result in the tracking process.
The method comprises the following steps of screening, packaging and uploading the collected cell image data by using an image definition algorithm, and returning to a waiting acquisition state after receiving an acquisition stopping instruction, wherein the method comprises the following steps:
and screening cell images with set definition in each set by using an image definition algorithm according to the cell set image data output from the convolutional neural network deep learning model, packaging and uploading all screened cell images with set definition to a central processing unit, clearing all image data cached after preprocessing until receiving a collection stopping instruction, exiting a continuous image data processing state, and returning to a state waiting for collection.
The invention relates to a clear image acquisition method for a cell of a sheath flow microscopic full-thickness runner, which comprises the steps of acquiring and checking the initial position of an objective lens displacement platform and the states of a camera and a flash lamp after power-on reset, emptying cached image data, simultaneously monitoring an acquisition starting instruction, driving the objective lens displacement platform to move after receiving the acquisition starting instruction, starting the camera acquisition according to a backlight synchronous control signal, caching the image data acquired based on a constraint relation, continuously acquiring real-time images, monitoring the cached data in real time, reading and preprocessing the cached image data, automatically tracking the same cell by using a convolutional neural network deep learning model, segmenting and extracting all tracked cell images, automatically collecting, screening and packaging the image data according to an image definition algorithm, uploading, and clearing all the image data cached after preprocessing, and when an image acquisition stopping instruction is received, the continuous image acquisition processing is quitted, and the state of waiting for idle acquisition is returned, so that the accuracy of cell acquisition and the image definition are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic step diagram of a clear image acquisition method for a sheath flow microscopic full-thickness flow channel cell provided by the invention.
FIG. 2 is a hardware schematic block diagram of the sheath flow microscopic full-thickness cell clear image extraction provided by the invention.
FIG. 3 is a flow chart of logic control and image acquisition provided by the present invention.
FIG. 4 is a flow chart of image data analysis and processing provided by the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1 to 4, the present invention provides a method for collecting clear images of cells in a sheath flow microscopic full-thickness flow channel, comprising:
s101, obtaining and checking an initial state, clearing a cache image and monitoring a start acquisition instruction.
Specifically, after power-on reset, an initial position of an objective lens displacement platform and states of a camera and a flash lamp are obtained and verified, a processing flow shown in fig. 3 comprises a logic control and image acquisition flow, if the initial position of the objective lens displacement platform or the states of the camera and the flash lamp are detected to be abnormal, an alarm signal is sent out, verification is conducted again until the initial position of the objective lens displacement platform and the states of the camera and the flash lamp are detected to be normal, cached image data are emptied, a system enters an idle acquisition waiting state, and after receiving an acquisition operation instruction, a central processing unit sends an acquisition starting instruction, wherein as shown in a hardware principle block diagram of clear image extraction of sheath flow microscopic full-thickness cells provided by fig. 2, the objective lens displacement platform is a mechanical combination of the displacement platform and the objective lens, the microscope objective lens is driven to slightly move in the optical axis direction, and the displacement precision, realize the imaging of the cells at different object distances. The displacement drive can be selected from piezoelectric ceramics or a voice coil motor, the highest displacement precision of the piezoelectric ceramics can realize the nanometer level, the response frequency can reach the kHz, the piezoelectric ceramics is generally applied to the micro-focusing occasions with high dynamic response speed and high magnification, but the cost is expensive, the displacement precision of the voice coil motor is basically in the mum level, the highest response frequency can also reach hundreds of Hz, the cost is relatively low, the piezoelectric ceramics is adopted as the displacement drive in the embodiment, the maximum displacement stroke is 80μm, the backlight lamps are mainly used for imaging illumination of the biological micro-bright field, LEDs or flash lamps can be adopted, if the LED lamps are adopted, cameras with short exposure time delay are required to be selected, the backlight illumination of the flash lamps has no requirement on the exposure time of the cameras, the backlight illumination of the flash lamps is adopted, the cameras can adopt CCD/CMOS cameras, and TDI cameras can also be adopted without cost, the acquisition frame rate and the data transmission interface can be selected according to the practical application of the project, and the experiment adopts a camera interface with camera parameters of 200W pixels, resolution of 2048 x 1080, pixel size of 5.5 μm x 5.5 μm, 340 fps. The imaging lens cone is a standard optical 1X magnifying lens cone, and the length is 18.5 cm. The invention solves the problems of fluorescence synchronous imaging control complexity and cell missing caused by synchronous failure; the cost of image acquisition hardware is reduced; the reliability of the imaging system is improved.
And S102, receiving the acquisition starting instruction, driving the objective lens displacement platform to move, and starting the camera to acquire according to the backlight synchronous control signal.
Specifically, after receiving an acquisition starting instruction transmitted by the central processing unit, the image acquisition control card drives the objective lens displacement platform to reciprocate according to a set frequency and a set stroke, wherein in order to ensure stable and smooth movement of the objective lens displacement platform, a displacement driving signal of the image acquisition control card adopts sine wave control, meanwhile, the image acquisition control card outputs a backlight synchronous control signal and starts a camera to acquire images, the image acquisition control card adopts FPGA as a main control chip, high-speed image data processing and synchronous control of each unit module are conveniently realized in a parallel mode, because the sheath flow device adopts an imaging plane sheath flow device, samples and sheath liquid are respectively injected into the left side of the sheath flow device, the flow ratio of a sheath sample is more than 10:1, the flow rate of liquid in a middle imaging area flow channel of the sheath flow device must be matched with the whole imaging system, the shot images cannot have motion smear and the sampling requirement of the highest frame rate of, meanwhile, the sample and sheath fluid are fed in and out by the fluid path auxiliary control unit.
S103, storing the image data acquired based on the constraint relation into a data cache region, and continuously acquiring the images in real time.
In particular, the flow velocity V of the sheath flow is determined according to the flow channel of the imaging areaoThe thickness H of the cell layer in the flow channel of the imaging area and the optical depth of field DtotWidth W of camera CCD, width W of camera CCD pixel size, and camera acquisition frame rate FsThe system comprises an objective displacement platform, a data frame receiving and writing area, a sampling time N and a constraint relation between the objective displacement platform, wherein the data frame receiving and writing area is carried out on image data collected based on the constraint relation, the real-time image collection is carried out continuously until the collection is finished, and the power-on reset state is returned, the maximum displacement, the operation frequency and the sampling time of the objective displacement platform ensure that when each cell flows through an imaging visual field area, the system can shoot an image which can be clearly imaged in a focal plane range, all cells on a cell layer of a flow channel can be scanned in the image collection process, the phenomenon that the cells are shot in a missing mode can not exist, the accuracy of cell counting absolute is ensured, clear image collection of full-thickness cells in a sheath flow channel is realized under medium and low speed sheath flow, and accurate cell absolute counting is realized.
Wherein the constraint relationship is as follows:
taking an integer multiple of sampling times N:
the sheath flow velocity Vo of the runner in the imaging area:
and a maximum displacement stroke S of the objective lens displacement platform:
the maximum displacement stroke S of the objective lens displacement platform is more than or equal to the thickness H of the cell layer in the imaging area of the flow channel
fourth, objective displacement platform reciprocating motion frequency f:
and S104, monitoring the image data in the data cache region, reading and caching the image data and preprocessing the image data.
Specifically, as shown in the image data analysis and processing flow shown in fig. 4, after receiving the acquisition start instruction downloaded by the central processing unit, the image acquisition control card enters a continuous image acquisition state, wherein the data processing logic flow monitors data in the buffer area in real time, reads the image data written into the buffer by the logic control and image acquisition flow, and performs preprocessing such as filtering and noise reduction and image enhancement on the image data, thereby reducing image noise interference and enhancing image quality.
And S105, carrying out cell tracking, segmentation and collection on the preprocessed image data by using the deep learning model.
Specifically, cell tracking and segmentation based on deep learning are realized by automatically tracking the same cell by utilizing the frontmost convolutional neural network deep learning model, segmenting and extracting all tracked cell images, and automatically collecting the images. In the tracking process, secondary constraint verification of coordinates and displacement is carried out on the output result of the tracking model of the deep learning module, so that the accuracy of cell tracking is ensured.
And S106, screening, packaging and uploading the collected cell image data by using an image definition algorithm until an acquisition stopping instruction is received, and returning to an acquisition waiting state.
Specifically, cell image data output from the convolutional neural network deep learning model is subjected to automatic contrast calculation on the same cell collection image data by using an image definition algorithm, image data with set definition, namely optimal definition, is screened out, images with optimal definition screened out by all cells are packaged and uploaded to a central processing unit, all image data cached after being processed are cleared, then the next round of image analysis and processing flow is repeatedly entered until an image acquisition stopping instruction is received, a continuous image acquisition state is quitted, then the next image acquisition starting instruction is waited to be received again, and the accuracy and the image definition of cell acquisition are improved.
The invention relates to a method for collecting clear images of cells in a sheath flow microscopic full-thickness runner, which comprises the steps of obtaining and checking the initial position of an objective lens displacement platform and the states of a camera and a flash lamp after power-on reset, emptying cached image data, monitoring a collection starting operation instruction by a system, receiving the collection starting instruction, driving the objective lens displacement platform to move, starting the camera to collect according to a backlight synchronous control signal, entering a continuous image collection processing state, caching the image data collected based on a constraint relation, continuously collecting the images in real time, monitoring the image data in a cache area in real time, reading the cached image data and preprocessing the image data, automatically tracking the same cell by using a convolutional neural network deep learning model, segmenting and extracting all tracked cell images, automatically collecting, screening and packaging and uploading the cell image data by using an image definition algorithm, and clearing all image data cached after the preprocessing, and repeatedly entering the next round of cell image acquisition, analysis and processing flow until an image acquisition stopping instruction is received, so that the accuracy of cell acquisition and the image definition are improved.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A clear image acquisition method for a sheath flow microscopic full-thickness flow channel cell is characterized by comprising the following steps:
acquiring and checking an initial state, clearing a cache image and monitoring a start acquisition instruction;
receiving the acquisition starting instruction, driving the objective lens displacement platform to move, and starting the camera to acquire according to the backlight synchronous control signal;
storing the image data acquired based on the constraint relation into a data cache region, and continuously acquiring images in real time;
monitoring the image data in the data cache region, reading and caching the image data and preprocessing the image data;
carrying out cell tracking, segmentation and collection on the preprocessed image data by using a deep learning model;
and screening, packaging and uploading the collected cell image data by using an image definition algorithm until an acquisition stopping instruction is received, and returning to a waiting acquisition state.
2. The method for collecting clear images of cells in sheath flow microscopy full-thickness flow channels according to claim 1, wherein the steps of obtaining and verifying the initial state, clearing the buffered images and monitoring the start-up collection command comprise:
after power-on reset, the initial position of the objective lens displacement platform and the states of the camera and the flash lamp are obtained, after the states are detected normally, the initial position of the objective lens is verified, cached image data are emptied, and an acquisition starting instruction is monitored in real time.
3. The method for collecting clear images of cells in the sheath flow microscopy full-thickness flow channel as claimed in claim 2, wherein receiving the collection starting command, driving the objective lens displacement platform to move, and starting the camera to collect the clear images according to the backlight synchronous control signal comprises:
and after receiving the acquisition starting instruction, controlling an image acquisition control card to drive the objective lens displacement platform to reciprocate according to the set frequency and stroke, simultaneously outputting a backlight synchronous control signal, and starting a camera to acquire images.
4. The method for collecting clear images of cells in the sheath flow microscopic full-thickness flow channel according to claim 3, wherein the step of storing the image data collected based on the constraint relationship into a data buffer area for continuous real-time image collection comprises:
according to the constraint relation among the imaging area flow channel sheath flow velocity, the imaging area flow channel cell layer thickness, the optical depth of field, the camera CCD width, the camera CCD pixel size width, the camera acquisition frame rate, the sampling times and the objective lens displacement platform, receiving data frames of image data acquired based on the constraint relation, writing the data frames into a cache area until the acquisition is finished, and returning to a power-on reset state.
5. The method for collecting clear images of cells in sheath flow microscopy full-thickness flow channels according to claim 4, wherein the monitoring of image data in the data buffer area, the reading and buffering of the image data and the preprocessing of the image data comprise:
and after receiving the acquisition starting instruction, entering a continuous image processing state, monitoring data in a data cache region in real time, reading the cached image data if the image data is not processed, and performing filtering, noise reduction and image enhancement processing on the image data.
6. The method for collecting clear images of cells in sheath flow microscopy full-thickness flow channel as claimed in claim 5, wherein the step of performing cell tracking, segmentation and collection on the preprocessed image data by using the deep learning model comprises:
and automatically tracking the same cell in the preprocessed image by using a convolutional neural network deep learning model, segmenting and extracting all tracked cell images, automatically collecting, and carrying out secondary constraint verification on coordinates and displacement of an output result in the tracking process.
7. The method for collecting clear images of cells in the sheath flow microscopic full-thickness flow channel according to claim 6, wherein the step of screening, packaging and uploading the collected cell image data by using an image definition algorithm until the cell image data returns to a waiting-to-collect state after receiving an acquisition stopping instruction comprises the steps of:
and screening cell images with set definition in each set by using an image definition algorithm according to the cell set image data output from the convolutional neural network deep learning model, packaging and uploading all screened cell images with set definition to a central processing unit, clearing all image data cached after preprocessing until receiving a collection stopping instruction, exiting a continuous image data processing state, and returning to a state waiting for collection.
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| CN113092346A (en) * | 2021-04-06 | 2021-07-09 | 中国水利水电科学研究院 | Algae cell counting detection system and detection method thereof |
| CN113469863A (en) * | 2021-06-28 | 2021-10-01 | 平湖莱顿光学仪器制造有限公司 | Method and device for acquiring microscopic image |
| CN113469863B (en) * | 2021-06-28 | 2024-04-26 | 平湖莱顿光学仪器制造有限公司 | Method and equipment for acquiring microscopic image |
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