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WO2025236925A1 - Procédé et appareil de détection de cellules tumorales circulantes, dispositif et support - Google Patents

Procédé et appareil de détection de cellules tumorales circulantes, dispositif et support

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Publication number
WO2025236925A1
WO2025236925A1 PCT/CN2025/087761 CN2025087761W WO2025236925A1 WO 2025236925 A1 WO2025236925 A1 WO 2025236925A1 CN 2025087761 W CN2025087761 W CN 2025087761W WO 2025236925 A1 WO2025236925 A1 WO 2025236925A1
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WIPO (PCT)
Prior art keywords
target
initial
detection
interest
feature map
Prior art date
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PCT/CN2025/087761
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English (en)
Chinese (zh)
Inventor
印明柱
胡春兰
赖仁胜
吴粤松
张宗波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University Three Gorges Hospital
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Chongqing University Three Gorges Hospital
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Publication of WO2025236925A1 publication Critical patent/WO2025236925A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting

Definitions

  • This invention relates to the field of biomedical technology, and in particular to methods, devices, equipment and media for detecting circulating tumor cells.
  • Circulating tumor cells are cancer cells shed from the primary tumor in the early stages of development and can spread to lymph nodes, bone marrow, or peripheral blood. Numerous studies have shown that CTCs can be used to predict cancer cell metastasis, and even disease progression and survival time in early-stage cancer patients. They can serve as biomarkers for cancer cells in clinical trials, helping to identify therapeutic targets and drug resistance mechanisms.
  • CTCs CTCs
  • concentration in peripheral blood is very low, with only 1 to 10 cells per 10 ml in most cancer patients.
  • Current detection methods include immunofluorescence, flow cytometry, and nucleic acid-based methods; however, these methods are time-consuming and subjective, leading to a high false-negative rate.
  • the purpose of this invention is to provide a method, apparatus, device, and medium for detecting circulating tumor cells, thereby improving the efficiency and accuracy of circulating tumor cell detection.
  • the specific solution is as follows:
  • this application discloses a method for detecting circulating tumor cells, comprising:
  • the feature map of the region of interest is input into the multi-level target mining and detection model; wherein, the multi-level target mining and detection model contains multiple detection heads;
  • the self-attention mechanism module in the target multi-level mining detection model divides each self-attention region of interest in the feature map of the region of interest into initial positive samples and initial negative samples.
  • the detection heads are used to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and the target positive samples are determined as the circulating tumor cell detection results of the target object.
  • acquiring the target optical image of the object to be detected includes:
  • a stained digital slide of the object to be tested is acquired, and initial optical images of the digital slide under different shooting conditions are acquired.
  • obtaining the region of interest feature map using the multi-scale feature map of the target optical image includes:
  • Convolutional features of the target optical image are extracted using a backbone network based on a feature pyramid network to obtain a multi-scale feature map;
  • the multi-scale feature map is input into the region proposal network to obtain the region of interest feature map.
  • the method before inputting the region of interest feature map into the target multi-level mining detection model, the method further includes:
  • the detection heads in the initial multi-level mining detection model are trained using the training dataset to obtain the detection heads in the current multi-level mining detection model.
  • the target multi-level mining detection model is obtained by back-optimizing each detection head of the current multi-level mining detection model based on the minimum loss function.
  • the step of dividing each self-attention region of interest in the feature map of the region of interest into initial positive samples and initial negative samples through the self-attention mechanism module in the multi-level target mining detection model includes:
  • the self-attention mechanism module of the initial detection head in the target multi-level mining detection model generates each self-attention region of interest in the feature map of the region of interest, and divides each self-attention region of interest into initial positive samples and initial negative samples according to the intersection-union ratio of each self-attention region of interest.
  • the step of generating each self-attention region of interest (ROI) of the ROI feature map through the self-attention mechanism module of the initial detection head in the target multi-level mining detection model includes:
  • the self-attention mechanism module of the initial detection head in the target multi-level mining detection model performs matrix transformation on the feature map of the region of interest to obtain the target matrix.
  • the target matrix is then transformed into a query vector, a key vector, and a value vector.
  • the SoftMax function is used to convert the product of the query vector and the key vector into a self-attention score.
  • the matrix product between the self-attention score and the value vector is obtained.
  • the matrix product is then convolved to obtain the dimension-restored matrix.
  • the dimension-restored matrix and the feature map of the region of interest are then added point by point to obtain each self-attention region of interest in the feature map of the region of interest.
  • the step of using each of the detection heads to screen for target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples includes:
  • the difficult negative samples in the current initial sample are removed to obtain the current target sample. Then, the current target sample is input to the current detection head to obtain the next positive sample selected by the current detection head from the current target sample.
  • the current detection head is the last detection head, then the next positive sample is determined as the target positive sample containing circulating tumor cells;
  • the sampler is used to select the next initial sample from the next positive sample and the initial negative sample, and the next initial sample is updated to the current initial sample. Then, the process jumps back to the step of removing the difficult-to-distinguish negative samples from the current initial sample.
  • this application discloses a circulating tumor cell detection device, comprising:
  • the feature map acquisition module is used to acquire the target optical image of the object to be detected, and to acquire the region of interest feature map using the multi-scale feature map of the target optical image.
  • the feature map input module is used to input the feature map of the region of interest into the target multi-level mining detection model; wherein, the target multi-level mining detection model includes multiple detection heads;
  • the detection result determination module is used to divide each self-attention region of interest in the feature map of the region of interest into initial positive samples and initial negative samples through the self-attention mechanism module in the multi-level target mining detection model, and to use each of the detection heads to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and to determine the target positive samples as the circulating tumor cell detection result of the object to be detected.
  • an electronic device including:
  • Memory used to store computer programs
  • a processor is configured to execute the computer program to implement the steps of the aforementioned disclosed method for detecting circulating tumor cells.
  • this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed circulating tumor cell detection method.
  • This application acquires a target optical image of the object to be detected, and uses the multi-scale feature map of the target optical image to obtain a region of interest feature map; the region of interest feature map is input into a target multi-level mining detection model; wherein, the target multi-level mining detection model contains multiple detection heads; through the self-attention mechanism module in the target multi-level mining detection model, each self-attention region of interest in the region of interest feature map is divided into initial positive samples and initial negative samples, and each of the detection heads is used to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and the target positive samples are determined as the circulating tumor cell detection result of the object to be detected.
  • this application inputs the feature map of the region of interest of the object to be detected into the target multi-level mining detection model to obtain the final target positive sample containing circulating tumor cells, that is, to obtain the circulating tumor cell detection result of the object to be detected.
  • using the model for automated circulating tumor cell detection can improve the efficiency of circulating tumor cell detection and reduce the detection time.
  • the target multi-level mining detection model contains multiple detection heads, which can improve the classification ability of the target multi-level mining detection model. It can screen out the target positive sample containing circulating tumor cells from the initial positive sample and the initial negative sample that are not accurate enough, thereby improving the accuracy of circulating tumor cell detection.
  • Figure 1 is a flowchart of a circulating tumor cell detection method disclosed in this application.
  • Figure 2 is a schematic diagram of a specific self-attention region of interest acquisition disclosed in this application.
  • Figure 3 is a schematic diagram of a specific multi-level target mining and detection model disclosed in this application.
  • Figure 4 is a schematic diagram of the evaluation of a specific multi-level mining detection model disclosed in this application.
  • Figure 5 is a schematic diagram of the structure of a circulating tumor cell detection device disclosed in this application.
  • Figure 6 is a structural diagram of an electronic device disclosed in this application.
  • Circulating tumor cells are cancer cells shed from the primary tumor in the early stages of cancer development and can spread to lymph nodes, bone marrow, or peripheral blood. Numerous studies have shown that CTCs can be used to predict cancer cell metastasis, and even disease progression and survival time in early-stage cancer patients. They can serve as biomarkers for cancer cells in clinical trials, identifying therapeutic targets and drug resistance mechanisms.
  • CTCs CTCs
  • concentration in peripheral blood is very low, with only 1 to 10 cells per 10 ml in most cancer patients.
  • Current detection methods include immunofluorescence, flow cytometry, and nucleic acid-based methods; however, these methods are time-consuming and subjective, leading to a high false-negative rate.
  • this application provides a circulating tumor cell detection scheme to improve the efficiency and accuracy of circulating tumor cell detection.
  • this application discloses a method for detecting circulating tumor cells, including:
  • Step S11 Acquire the target optical image of the object to be detected, and use the multi-scale feature map of the target optical image to obtain the region of interest feature map.
  • acquiring the target optical image of the object to be tested includes: acquiring a stained digital slide of the object to be tested, and acquiring initial optical images of the digital slide under different shooting conditions; adjusting the pixel size of each initial optical image to obtain the target optical image.
  • the object to be tested is peripheral blood from the patient's testing site, which may contain circulating tumor cells.
  • the object to be tested is stained with H&E (Hematoxylin and eosinstains) or methylene blue to obtain a stained digital slide.
  • the digital slide is then photographed under an optical microscope at a fixed magnification under different shooting conditions to obtain initial optical images. For example, the digital slide is horizontally flipped, vertically flipped, and the image brightness is adjusted before photographing to obtain multiple initial optical images.
  • the short side of each initial optical image is then uniformly adjusted to 800 pixels to obtain the target optical image of the object to be tested, thereby reducing the computational load.
  • obtaining the region of interest (ROI) feature map using the multi-scale feature map of the target optical image includes: extracting the convolutional features of the target optical image using a backbone network based on a feature pyramid network to obtain a multi-scale feature map; and inputting the multi-scale feature map into a region proposal network to obtain a region of interest (ROI) feature map.
  • the target optical image is input into a backbone network based on a feature pyramid network (FPN), which extracts the convolutional features of the input image to obtain a multi-scale feature map.
  • FPN feature pyramid network
  • This multi-scale feature map is then input into a region proposal network (RPN) to obtain the region of interest (ROI) feature map, i.e., ROIs.
  • RPN region proposal network
  • Step S12 Input the feature map of the region of interest into the target multi-level mining detection model; wherein the target multi-level mining detection model contains multiple detection heads.
  • the method before inputting the region of interest feature map into the target multi-level mining detection model, the method further includes: training each detection head in the initial multi-level mining detection model using a training dataset to obtain each detection head of the current multi-level mining detection model; and performing reverse optimization on each detection head of the current multi-level mining detection model based on the minimum loss function to obtain the target multi-level mining detection model.
  • the initial detection head i.e., the first-level detection head
  • the initial detection head includes a self-attention mechanism module, while the remaining detection heads do not.
  • All detection heads also include a sampler classifier h ⁇ sub> t ⁇ /sub> and a localization parameter regressor f ⁇ sub> t ⁇ /sub> .
  • the parameters of each detection head are optimized by minimizing the loss function. The losses of all detection heads are then aggregated to train the model.
  • g represents the true value assigned to x t
  • [ ⁇ ] is the index function
  • b t is the predicted box coordinates of the parametric regressor f t-1 (x t-1 , b t-1 ) of the previous detector or the suggestion box coordinates generated by the RPN network.
  • the type of coordinates used can be determined based on t.
  • Step S13 The self-attention mechanism module in the target multi-level mining detection model divides each self-attention region of interest in the feature map of the region of interest into initial positive samples and initial negative samples.
  • the detection heads are used to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and the target positive samples are determined as the circulating tumor cell detection results of the object to be detected.
  • the step of dividing the self-attention regions of interest (ROIs) of the region of interest feature map into initial positive samples and initial negative samples through the self-attention mechanism module in the multi-level target mining detection model includes: generating each ROI of the region of interest feature map through the self-attention mechanism module of the initial detection head in the multi-level target mining detection model, and dividing each ROI into initial positive samples and initial negative samples according to the intersection-over-union (IoU) ratio of each ROI.
  • the self-attention mechanism module of the initial detection head generates each ROI of the region of interest feature map and calculates the intersection-over-union (IoU) ratio of each ROI. Based on the IoU ratio, each ROI is divided into initial positive samples and initial negative samples, as detailed below:
  • y is the sample label
  • g y is the class label of the ground value g
  • x is a proposal (i.e., the region of interest for self-attention)
  • u is the IoU threshold
  • y t is the label x t given by the formula for separating positive and negative samples.
  • Intersection over Union (IoU) ratio is less than the threshold u, it is considered an initial negative sample; if the IoU ratio is not less than the threshold u, it is considered an initial positive sample.
  • predicted boxes with an IoU ratio not less than the threshold are classified as positive samples, and their corresponding labels are positive samples. Boxes with an IoU ratio less than the threshold are classified as negative samples, and their corresponding labels are negative samples.
  • the initial positive samples contain difficult-to-classify positive samples (i.e., incorrectly labeled positive samples) and easy-to-classify positive samples (i.e., correctly labeled positive samples)
  • the initial negative samples contain difficult-to-classify negative samples (i.e., incorrectly labeled negative samples) and easy-to-classify negative samples (i.e., correctly labeled negative samples).
  • the step of generating each self-attention region of interest (ROI) of the ROI feature map through the self-attention mechanism module of the initial detection head in the target multi-level mining detection model includes: performing matrix transformation on the ROI feature map through the self-attention mechanism module of the initial detection head in the target multi-level mining detection model to obtain a target matrix, and converting the target matrix into a query vector, a key vector, and a value vector; using the SoftMax function to convert the product of the query vector and the key vector into a self-attention score; obtaining the matrix product between the self-attention score and the value vector; performing convolution on the matrix product to obtain a dimension-restored matrix; and adding the dimension-restored matrix and the ROI feature map point by point to obtain each self-attention ROI of the ROI feature map.
  • Figure 2 shows a specific schematic diagram of self-attention ROI acquisition. The specific process of self-attention ROI acquisition is as follows:
  • the self-attention mechanism module performs matrix transformation on the feature map of the region of interest to obtain the target matrix. That is, the number and size of the feature map of the region of interest are represented as F N and BN ⁇ C ⁇ h ⁇ w, where B is the number of images input to the neural network at one time, C is the number of channels, h is the height of the feature, and w is the width of the feature.
  • q, k, and v represent the query vector, key vector, and value vector, respectively;
  • f( ⁇ ) represents the convolution operation; and the Reshape( ⁇ ) function flattens the feature matrix into a vector.
  • ⁇ ( ⁇ ) represents the SoftMax function, which is a probability distribution that maps the scores to the range [0, 1].
  • fRe:conv 1 ⁇ 1 is the convolution operation used to recover the original channel dimension of the feature space
  • F′N is the attention-based region of interest (i.e., AttROIs).
  • the step of using each of the detection heads to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples includes: based on a preset positive-negative sample ratio, using a sampler to screen out the current initial sample from the initial positive samples and the initial negative samples, removing difficult-to-distinguish negative samples from the current initial sample to obtain the current target sample, and then inputting the current target sample into the current detection head to obtain the next positive sample screened out by the current detection head from the current target sample; if the current detection head is the last detection head, then the next positive sample is determined as the target positive sample containing circulating tumor cells; if the current detection head is not the last detection head, based on the preset positive-negative sample ratio, using the sampler to screen out the next initial sample from the next positive sample and the initial negative samples, updating the next initial sample to the current initial sample, and then jumping back to the step of removing difficult-to-distinguish negative samples from the current initial sample.
  • Figure 3 shows a schematic diagram of a specific multi-level target mining detection model.
  • the number of initial positive samples is much smaller than the number of initial negative samples.
  • a balanced positive-negative sampler is used to randomly generate positive and negative samples from all proposals at a specified ratio. Based on a preset positive-negative sample ratio, the sampler filters the current initial samples from the initial positive and negative samples. Difficult-to-distinguish positive and negative samples from the initial positive and negative samples are preferentially selected as the current initial samples. Difficult-to-distinguish negative samples are removed from the current initial samples to obtain the current target sample. This means that the detection head in subsequent stages corrects the classification results predicted in previous stages, performing multiple identifications and corrections on difficult-to-distinguish samples to optimize detection performance.
  • the current target sample is input into the current detection head.
  • the current detection head selects the next positive sample from the current target sample. If the current detection head is not the last detection head, then based on the preset positive and negative sample ratio, the next initial sample is selected from the next positive sample and the initial negative sample using a sampler. The next initial sample is then updated to the current initial sample. Then, the process jumps back to the step of removing the difficult negative samples from the current initial sample.
  • the difficult sample prediction boxes of the previous stage detection head are preferentially used as the proposal input for the next stage detection head, and positive and negative samples are extracted proportionally by the positive and negative sample sampler.
  • T is the total number of ladder levels
  • T-1 is the total number of ladder levels minus 1
  • x is a proposal
  • b is the coordinate of the proposal.
  • the multi-level target mining detection model includes two modules: a self-attention module for enhancing the ROI and a hard sample mining sampler.
  • the self-attention mechanism module is used to improve the performance and accuracy of target detection, while the hard sample sampler is used in non-initial detection heads to identify and correct hard samples multiple times to optimize detection performance.
  • This application acquires a target optical image of the object to be detected, and uses the multi-scale feature map of the target optical image to obtain a region of interest feature map; the region of interest feature map is input into a target multi-level mining detection model; wherein, the target multi-level mining detection model contains multiple detection heads; through the self-attention mechanism module in the target multi-level mining detection model, each self-attention region of interest in the region of interest feature map is divided into initial positive samples and initial negative samples, and each of the detection heads is used to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and the target positive samples are determined as the circulating tumor cell detection result of the object to be detected.
  • this application inputs the feature map of the region of interest of the object to be detected into the target multi-level mining detection model to obtain the final target positive sample containing circulating tumor cells, that is, to obtain the circulating tumor cell detection result of the object to be detected.
  • using the model for automated circulating tumor cell detection can improve the efficiency of circulating tumor cell detection and reduce the detection time.
  • the target multi-level mining detection model contains multiple detection heads, which can improve the classification ability of the target multi-level mining detection model. It can screen out the target positive sample containing circulating tumor cells from the initial positive sample and the initial negative sample that are not accurate enough, thereby improving the accuracy of circulating tumor cell detection.
  • Figure 4 shows a specific evaluation diagram of a multi-level mining detection model.
  • the accuracy of the multi-level mining detection model is evaluated, including average accuracy, receiver operating characteristic curve (ROC) analysis, and ablation experiments, as detailed below:
  • ROC receiver operating characteristic curve
  • Average precision can simultaneously reflect the recall and detection precision for each class, and is defined as follows:
  • k represents the number of CTC cells or CTC-like cells detected
  • P(k) represents the precision up to the kth element in the list
  • ⁇ r(k) represents the recall from the (k-1)th to the kth item
  • TP, FP, and FN represent the number of predicted boxes for true positives, false positives, and false negatives, respectively.
  • the mAP value is also used as an indicator, which is the average AP value for all classes.
  • FROC free-response operating characteristic curve
  • the accuracy and reliability of the multi-level target mining detection model can be verified by evaluating the average accuracy, free acceptor operation characteristic curve analysis, and ablation experiments.
  • this application discloses a circulating tumor cell detection device, comprising:
  • Feature map acquisition module 11 is used to acquire the target optical image of the object to be detected, and to acquire the region of interest feature map using the multi-scale feature map of the target optical image;
  • the feature map input module 12 is used to input the feature map of the region of interest into the target multi-level mining detection model; wherein, the target multi-level mining detection model includes multiple detection heads;
  • the detection result determination module 13 is used to divide each self-attention region of interest in the feature map of the region of interest into initial positive samples and initial negative samples through the self-attention mechanism module in the multi-level target mining detection model, and to use each of the detection heads to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and to determine the target positive samples as the circulating tumor cell detection result of the object to be detected.
  • This application acquires a target optical image of the object to be detected, and uses the multi-scale feature map of the target optical image to obtain a region of interest feature map; the region of interest feature map is input into a target multi-level mining detection model; wherein, the target multi-level mining detection model contains multiple detection heads; through the self-attention mechanism module in the target multi-level mining detection model, each self-attention region of interest in the region of interest feature map is divided into initial positive samples and initial negative samples, and each of the detection heads is used to screen out target positive samples containing circulating tumor cells from the initial positive samples and the initial negative samples, and the target positive samples are determined as the circulating tumor cell detection result of the object to be detected.
  • this application inputs the feature map of the region of interest of the object to be detected into the target multi-level mining detection model to obtain the final target positive sample containing circulating tumor cells, that is, to obtain the circulating tumor cell detection result of the object to be detected.
  • using the model for automated circulating tumor cell detection can improve the efficiency of circulating tumor cell detection and reduce the detection time.
  • the target multi-level mining detection model contains multiple detection heads, which can improve the classification ability of the target multi-level mining detection model. It can screen out the target positive sample containing circulating tumor cells from the initial positive sample and the initial negative sample that are not accurate enough, thereby improving the accuracy of circulating tumor cell detection.
  • FIG. 6 is a structural diagram of an electronic device 20 according to an exemplary embodiment; the content in the figure should not be construed as limiting the scope of this application.
  • Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Specifically, it may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input/output interface 25, and a communication bus 26.
  • the memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the circulating tumor cell detection method performed by the electronic device disclosed in any of the foregoing embodiments.
  • the power supply 23 is used to provide operating voltage for various hardware devices on the electronic device;
  • the communication interface 24 can create a data transmission channel between the electronic device and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here;
  • the input/output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.
  • the processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor.
  • the processor 21 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).
  • the processor 21 may also include a main processor and a coprocessor.
  • the main processor also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.
  • the processor 21 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen.
  • the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.
  • AI Artificial Intelligence
  • the memory 22 as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc.
  • the resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.
  • the operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22.
  • the operating system can be Windows, Unix, Linux, etc.
  • the computer program 222 in addition to including a computer program capable of performing the circulating tumor cell detection method executed by the electronic device as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks.
  • the data 223 may include data received by the electronic device from external devices, as well as data collected by its own input/output interface 25.
  • this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for detecting circulating tumor cells. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.
  • the software module may be located in random access memory (RAM), memory, read-only memory (ROM), electrically programmable EPROM (EPROM), electrically erasable programmable read-only memory (EEPROM), register, hard disk, removable disk, CD-ROM (Compact Disc Read-Only Memory), or any other form of storage medium known in the art.
  • RAM random access memory
  • ROM read-only memory
  • EPROM electrically programmable EPROM
  • EEPROM electrically erasable programmable read-only memory
  • register hard disk, removable disk
  • CD-ROM Compact Disc Read-Only Memory

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  • Investigating Or Analysing Biological Materials (AREA)
  • Image Analysis (AREA)

Abstract

La présente demande se rapporte au domaine technique de la biomédecine et divulgue un procédé et un appareil de détection de cellules tumorales circulantes, ainsi qu'un dispositif et un support. Le procédé comprend les étapes consistant à : acquérir une image optique cible d'un objet en cours de détection et utiliser une carte de caractéristiques à échelles multiples de l'image optique cible pour obtenir une carte de caractéristiques d'une région d'intérêt ; entrer la carte de caractéristiques de la région d'intérêt dans un modèle de détection d'exploration à niveaux multiples cible, le modèle de détection d'exploration à niveaux multiples cible comprenant une pluralité de têtes de détection ; et regrouper des régions d'auto-attention d'intérêt dans la carte de caractéristiques de la région d'intérêt en échantillons positifs initiaux et en échantillons négatifs initiaux par l'intermédiaire d'un module de mécanisme d'auto-attention dans le modèle de détection d'exploration à niveaux multiples cible, utiliser les têtes de détection pour cribler les échantillons positifs initiaux et les échantillons négatifs initiaux de façon à obtenir un échantillon positif cible contenant des cellules tumorales circulantes et déterminer l'échantillon positif cible comme un résultat de détection de cellules tumorales circulantes relatif à l'objet en cours de détection. L'utilisation du modèle pour effectuer une détection automatisée de cellules tumorales circulantes peut améliorer l'efficacité et la précision de la détection de cellules tumorales circulantes et réduire le temps requis pour une détection.
PCT/CN2025/087761 2024-05-17 2025-04-08 Procédé et appareil de détection de cellules tumorales circulantes, dispositif et support Pending WO2025236925A1 (fr)

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CN202410617443.9A CN118379732A (zh) 2024-05-17 2024-05-17 循环肿瘤细胞检测方法、装置、设备及介质
CN202410617443.9 2024-05-17

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WO2025236925A1 true WO2025236925A1 (fr) 2025-11-20

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