40 results about "Tumor Pathology" patented technology

The invention discloses a multi-modal tumor identification method based on a frequency domain attention mechanism. The method comprises the following steps: S1, acquiring multi-frequency bioelectrical impedance signals of tumor tissues and normal tissues and corresponding medical tumor pathological images; s2, extracting a frequency-time feature of the electrical impedance signal and a spatial feature of the pathological image through a multi-modal feature coding module, and generating a multi-modal fusion feature; s3, converting the multi-modal fusion features into frequency domain representation, performing weighted enhancement on the multi-modal fusion features through a frequency domain attention module, and outputting the features; and S4, fusing the features output by the frequency domain attention module and the superficial layer features in the coding stage through jump connection to obtain enhanced features, inputting the enhanced features into a decoding network, and outputting a tumor classification result. According to the method, the bioelectrical impedance signal and the multi-modal information of the pathological image are fused, and a frequency domain attention mechanism is introduced to effectively improve the expression ability of key frequency characteristics.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention is suitable for the field of medical image analysis, and provides a tumor pathological image segmentation method based on a U-Net neural network, and the method comprises the steps: extracting the gray, texture and shape multi-dimensional features of tumor cells through a U-Net encoder, constructing a feature cluster, building an invasion direction probability model based on the cluster, analyzing the morphological features of a necrotic region, and speculating the growth speed. And dynamically adjusting the segmentation sensitivity threshold of the decoder, and finally outputting a three-channel segmentation map containing a tumor core region, a false envelope invasion region and a capillary invasion region. According to the scheme, through multi-dimensional feature fusion and biological behavior modeling, precise characterization of tumor heterogeneity is achieved, the adaptability of a segmentation model to different invasion active areas is improved through a dynamic sensitivity regulation mechanism, a segmentation result with anatomical positioning and biological evaluation values is provided for clinic, and the segmentation accuracy is improved. And diagnosis and treatment decision of tumors are effectively assisted. The method is easy and convenient to operate and high in automation degree and has remarkable clinical application value.

The invention provides a method for inducing HL-60 cells to be differentiated into neutrophil-like cells, and belongs to the technical field of cell culture. According to the method, the induced differentiation efficiency of the HL-60 cells to the neutrophil-like cells (D-HL-60) and the cell quality are remarkably improved, high-proportion functional D-HL-60 cells can be obtained, a good motility rate can be maintained, and a stable and reliable cell model is provided for researching a neutrophil differentiation mechanism, screening related drugs and analyzing immune functions. Particularly, the functional D-HL-60 cells obtained on the basis of the method can be used as an ideal model, deep analysis of neutrophil trapping nets (NETs) in inflammation and tumor pathological mechanisms is assisted, and an experimental basis is provided for development of therapeutic strategies of targeted NETs.

The present invention relates to the field of pathology analysis technology, and more specifically to a tumor pathology analysis method based on multimodal artificial intelligence, comprising the following steps: acquiring multi-center, multi-type whole-slice tissue images, cutting the whole-slice images into pathology images, and dividing the standardized pre-processed pathology images into training and test sets; constructing a contrastive language and image model for capturing key features in pathology images through a two-stage pre-training process; and fine-tuning the model using the pre-trained weights of the contrastive language and image model as initial weights and adding task-specific fully connected layers to obtain a pathology analysis model for implementing different classification and regression tasks in tumor pathology analysis. By integrating multimodal information from images and text, the present invention can more comprehensively assess tumor type, stage, and prognosis, thereby fully leveraging the advantages of multi-source data and effectively addressing the current bottlenecks in tumor pathology analysis technology.

The invention relates to the technical field of image recognition, and particularly discloses an auxiliary blood tumor pathological diagnosis system and method based on artificial intelligence, and the system extracts a pathological image group of a patient through a pathological diagnosis auxiliary platform, analyzes the data of each pathological image, and judges the effective feature value of each pathological image; the effective feature values of the pathological images are compared with a predefined effective feature threshold value to obtain a comparison result, and the pathological diagnosis auxiliary platform judges whether the effective features of the pathological images are enhanced or not based on the comparison result; and extracting cell characteristic data in each pathological image according to each pathological image, judging the characteristic complexity of each pathological image, and performing difference analysis on the pathological image group to complete auxiliary blood tumor pathological diagnosis.

The invention discloses a survival prediction method based on a tumor pathological image, and the method comprises the steps: obtaining a key region of the tumor pathological image through deep learning and an attention mechanism, and obtaining a cell nucleus boundary feature, a cell nucleus texture feature and / or a cell nucleus image structure feature based on the key region of the tumor pathological image; and survival prediction is carried out through the trained machine learning model. According to the survival prediction method, the accuracy of survival prediction of tumor patients is improved, and meanwhile, the defect that a traditional deep learning method is poor in interpretability is overcome.

The present patent application relates to nanoparticles for the delivery and targeting of a non-tumour-specific antigen in cancer cells, comprising a matrix support based on a biocompatible material, the non-tumour-specific antigen, and an adjuvant, for use in recalling, in cancer patients who have a specific immunity for the non-tumour- specific antigen pre-existing to the tumour pathology, the immune response specific to the non-tumour-specific antigen against the cancer cells. A further object of the application is anti-tumour pharmaceutical formulations comprising nanoparticles and kits comprising the aforementioned anti-tumour pharmaceutical formulations in combination with traditional anti-tumour vaccines.

The invention relates to negative functional modulators of at least one variant of the non-erythrogenic erythropoietin (V-EPO) and pharmaceutical compositions or kits containing them. Such functional negative modulators of V-EPO may be a mono- or multi-specific antibody anti-EV3, anti-EV4, anti-EV1-4, anti-EV1-5, anti-EV1-1, or EV2-1, or anti-Epo receptor (EpoR) anti-EPHB4, anti-CSF2RB, an antisense oligonucleotide, DNA decoy, RNA decoy, a ribozyme, an antagomiR, a shRNA, LNA or siRNA.Several uses of these functional modulators are described, which have been advantageously employed as a medicament and for the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing organ or tissue transplantation.The invention also describes variants of EPO for use in the diagnosis and in the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing an organ or tissue transplantation and as a diagnostic agent.According to another aspect, a monoclonal antibody to at least one of the variants of the erythropoietin is described.According to yet another aspect, the use of at least one alternative splicing variant of non-erythrogenic EPO and the measurement thereof at tissue and / or systemic level is described, as well as the study of the methylation status of the promoters of the genes involved in the EPO signalling pathway (by way of example EPO, EPOR, EPHB4, CSF2RB), as diagnostic, prognostic and predictive markers of an oncological pathology, a proliferative pathology, neurodegenerative or inflammatory pathology.

The invention relates to the technical field of tumor pathological specimen fixing and preservation, in particular to a tumor pathological specimen fixer which comprises a fixing box, a storage drawer is inserted into the fixing box, and a locking mechanism for conveniently limiting the drawing position of the storage drawer is arranged in the fixing box. An identifying and fixing mechanism which is convenient for fixing and identifying specimens is arranged in the storage drawer, and a taking and placing mechanism which is convenient for quickly taking and placing the specimens in a non-contact manner and is convenient for taking and placing the specimens is arranged above the storage drawer. According to the tumor pathological specimen fixing device, a tumor pathological slide specimen can be conveniently clamped, stored and fixed through a movable cavity formed in a storage drawer and a lifting rod, a fixing sleeve and a buffering cushion strip arranged in a lifting groove, and a sliding seat and a second magnet are slidably connected to a sliding rail fixedly connected to the bottom of an inner cavity of the movable cavity; and the second magnet slides below the first magnet, so that the first magnet and the second magnet are conveniently utilized to jack up the fixed sleeve and the taking and placing mechanism for observation and taking.

The invention provides a tumor pathological image typing and grading method and device based on deep learning and a residual network technology, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a tumor pathological image, segmenting a tissue region, carrying out the color normalization, and dividing the tumor pathological image into image blocks; constructing a deep convolutional feature extraction network based on a residual network, and extracting shallow and deep feature maps; performing up-sampling on the deep feature map and performing weighted fusion on the deep feature map and the shallow feature map, and inputting the deep feature map and the shallow feature map into parallel convolution branches to obtain typing and grading feature maps; calculating a feature deviation degree based on the hierarchical feature map to generate a space attention map, and determining a region focusing degree; enhancing the typing feature map by using the attention weight; respectively carrying out global pooling and classification on the enhanced feature map and the graded feature map, and outputting a typing and grading result; screening high-focus points according to an area focusing degree set threshold value, and generating a key area thermodynamic diagram; according to the invention, typing and grading cooperative processing is realized, and the diagnosis efficiency and accuracy are improved.

The present invention discloses a bone tumor image enhancement method and system, comprising: step 1: acquiring a bone tumor pathology image, and performing fuzzy processing on the bone tumor pathology image, using the fuzzy processed bone tumor pathology image as an input data set, and using the original bone tumor pathology image as an output data set; step 2: constructing an image super-resolution enhancement model, and training the image super-resolution enhancement model using the input data set and the output data set until the loss function value of the image super-resolution enhancement model tends to converge; step 3: acquiring the bone tumor pathology image to be enhanced in real time, inputting the trained image super-resolution enhancement model, and generating a bone tumor pathology image with completed image super-resolution enhancement. The present invention solves the problem of image distortion of subtle pathological tissue parts in pathology images caused by conventional image super-resolution enhancement methods.

The present invention discloses a tumor pathology image segmentation method and system based on a graph convolutional contrastive learning network, which relates to the field of image segmentation technology. The method includes: obtaining a tumor pathology image and a segmentation label corresponding to the tumor pathology image, generating a tumor pathology image training set based on the tumor pathology image and the segmentation label corresponding to the tumor pathology image; preprocessing the tumor pathology image training set, inputting the preprocessed tumor pathology image training set into a pre-established offset graph convolutional contrastive learning network model for training, and obtaining a trained offset graph convolutional contrastive learning network model; receiving a tumor pathology image to be segmented, inputting the tumor pathology image to be segmented into the trained offset graph convolutional contrastive learning network model, and outputting a tumor pathology image segmentation result to achieve accurate image segmentation.

The invention relates to the technical field of medical instruments, and discloses a bone tumor pathological tissue sampling device integrated with a cutting function, which comprises a left side shell and a right side shell which are connected through a screw, and also comprises a cutting assembly and a tumor sampling assembly, the front ends of the left side shell and the right side shell are connected with end covers through screws, the end faces of the end covers are fixedly connected with sleeves, and the outer walls of the sleeves are fixedly connected with lug blocks and side blocks. Sampling and cutting functions are ingeniously combined, after cutting is completed, a buckle plate is pulled to drive a string, a micro winch and other parts, so that a biting block rotates around a vertical shaft to be close to a cutting blade, the biting block and the cutting blade are matched to bite tumor pathological tissue, and sawteeth on the side edge of the biting block facilitate easy sampling; by means of the integrated design, the sampling process of bone tumor pathological tissue is effectively simplified, the tedious step of switching multiple tools in traditional operation is avoided, and the sampling efficiency is greatly improved.

The present invention belongs to the field of medical device technology and discloses a tumor pathology section dehydration device, comprising a workbench, a mounting block mounted on the workbench via a locking assembly; a dehydration cylinder detachably connected to the mounting block, a dehydration box for containing tissue samples disposed in the dehydration cylinder; an emptying assembly comprising an emptying pipe disposed in an assembly slot, the top end of the emptying pipe being connected to the dehydration cylinder, and the bottom end of the emptying pipe being respectively connected to a waste liquid cylinder and an exhaust gas cylinder disposed in the workbench; a centrifugal assembly disposed on the workbench, the output end of the centrifugal assembly being transmission-connected to the dehydration box to drive the dehydration box for centrifugal dehydration; and a liquid inlet assembly disposed on the workbench and connected to the dehydration box. The present invention has a compact structure, a high degree of automation, and organically combines physical dehydration and chemical dehydration, thereby overcoming the shortcomings of physical dehydration and chemical dehydration, improving the dehydration efficiency and quality of tissue samples, reducing dehydration costs, and accelerating the efficiency and accuracy of determining tumor properties.

The invention discloses a brain tumor specimen space positioning and section manufacturing device, and belongs to the technical field of brain tumor pathological sections, and the brain tumor specimen space positioning and section manufacturing device comprises a bottom plate, a fixing frame, a directional puncture device, tumor forceps and a proofreading device. The system can automatically trigger and record the space coordinates at two key action points for clamping and releasing a tumor, accurately calculates the position and angle change of the tumor from the inside to the outside by calculating a space transformation matrix, solves the fundamental problem of in-vitro specimen orientation loss, and improves the accuracy of the in-vitro specimen. A two-stage posture adjusting system composed of a rotating assembly and an angle fine adjustment assembly is used for driving a puncture needle to conduct puncture and dyeing marking at the calculated accurate angle, the marking direction corresponds to the original direction of a tumor in a body, and a reliable space reference is provided for pathological analysis; the calibration device is arranged to perform multi-attitude high-precision calibration on the three-dimensional directional monitoring assembly of the tumor forceps.

The invention discloses an iodine contrast agent adverse reaction risk prediction method and system for tumor patients, and relates to the technical field of medical data processing and risk prediction. According to the technical key points, the method comprises the following steps of: constructing a tumor patient exclusive multi-dimensional time sequence data set, preprocessing data, constructing a tumor patient exclusive risk factor system, constructing a pharmacological perception drug embedding layer, constructing a multi-scale self-adaptive time sequence feature extraction core module, and coupling and modulating a tumor pathological state and a time sequence risk. Performing multi-feature fusion and risk prediction model training verification, risk prediction and interpretability clinical decision support and clinical iterative optimization of the model; by constructing a multi-dimensional data set, time sequence modes of different anti-tumor drugs in different short, medium and long risk periods are dynamically captured, and the specific pathological state of a tumor patient is used as a modulation factor to non-linearly amplify or weaken the risk intensity of a corresponding time window, so that the accuracy and clinical credibility of risk prediction are improved.

The invention relates to the technical field of tumor immunity, and discloses a sample treatment method for detecting a circulating mononuclear cell immune function and a tumor microenvironment macrophage immune function, which comprises the following steps: treating a peripheral blood sample and a tumor pathological tissue sample; according to the treatment method, the mononuclear cell purity is improved through CD14 magnetic bead sorting, the macrophage recovery rate is improved through mixed enzymatic hydrolysate, and the core improvement that mild reagents (a hypotonic hemolysis buffer solution and a BSA-containing stationary solution) protect the cell activity and the antigen integrity is achieved, so that the treatment method has the important significance in the aspects of the mononuclear cell purity, the macrophage recovery rate, the cell activity and the antigen fluorescence intensity. Compared with the prior art, the method provided by the invention has the advantages that the method provided by the invention has the advantages that the method provided by the invention is obviously superior to a conventional treatment method, the defects of low monocyte enrichment efficiency, poor macrophage recovery rate and insufficient cell activity and antigen integrity of the conventional method are overcome, and a higher-quality sample basis is provided for subsequent circulating monocyte subtype typing (Mo1-Mo4) and tumor microenvironment macrophage immune function detection.

The invention provides a tumor pathological image detection method. The tumor pathological image detection method comprises the following steps: sectioning an in-vitro tissue separated from a tumor focus along a preset plane to form a sectioning plane; the coloring agent is used for coloring the section cutting plane to form a to-be-detected object; translating, scanning and imaging along the profile contour of the to-be-detected object by using an imaging device so as to continuously obtain a plurality of local pathological images; and splicing the local pathological images according to the imaging sequence to form an annular image at least covering the profile contour, judging whether the annular image contains tumor cells or not through a machine learning algorithm, and judging that the in-vitro tissue contains a complete tumor when the annular image does not contain the tumor cells. According to the invention, the calculation cost for judging whether the annular image contains the tumor cells or not by a machine learning algorithm is remarkably reduced, and the detection time is remarkably shortened.

The present application relates to the technical field of necrosis region segmentation, and particularly relates to a kind of automatic segmentation method of digestive tract tumor pathological image necrosis region.The present application obtains the necrosis probability of each image block according to the pixel value distribution of different pixel points in each image block and the morphological characteristics of different regions;for any image block, according to the pixel value distribution of different regions, the necrosis probability and the preset pixel fixed step, obtain the new clustering center corresponding to the cytoplasm region in each image block, and the iteration clustering of all pixel points is carried out until the preset iteration number is reached, to obtain the optimized clustering center of cytoplasm region;according to the optimized clustering center corresponding to the cytoplasm region in each image block, clustering is carried out, the new necrosis probability of each image block is analyzed, and the necrosis block is screened out for segmentation.The present application improves the accuracy of segmentation by adaptively adjusting the clustering center in the clustering algorithm.

The invention provides a tumor prediction method based on a hierarchical visual language and electronic equipment, the method is applied to the field of artificial intelligence, and the method comprises the following steps: obtaining a three-dimensional medical image of a target patient and a tumor region of interest, key sub-region features of the region are extracted to obtain a local semantic feature set, tumor and adjacent tissue features are extracted to obtain a global context feature set, and the local semantic feature set and the global context feature set form a visual feature set; the method comprises the following steps: constructing clinical prior query of a target cancer species and an image mode, generating prognosis related language prompts through a large language model, encoding, solving an optimal matching plan matrix of a semantic matching optimal problem, and screening an elite feature subset. And enhancing a global feature set through gating fusion, applying a contrast learning consistency constraint, fusing features to form a representation vector, inputting the representation vector into a prediction layer, and obtaining a survival risk score. According to the method, priori knowledge and tumor pathological features can be fused, so that an accurate and reliable survival risk score is finally output, and the method is more reliable to adapt to clinical actual evaluation requirements.

The invention discloses a tumor chemotherapy effect prediction model construction method and system based on artificial intelligence. The method comprises the following steps: multi-dimensional data acquisition: acquiring tumor biological data, individual clinical heterogeneity data and patient report outcome data of a target cancer type patient; performing feature engineering processing: performing quantitative processing on the individual clinical heterogeneity data to generate quantitative indexes, and performing feature extraction on the tumor biological data to form a tumor microenvironment feature set; basic model training: taking multi-dimensional data of large-sample general cancer species as a training set, adopting a multi-branch feature fusion network to construct a general chemotherapy effect prediction basic model, and taking chemotherapy effect prediction and life quality influence prediction as double optimization targets for training, according to the invention, by collecting tumor biological data, individual clinical heterogeneity data and patient report outcome data, a multi-dimensional data system is constructed, and tumor pathological characteristics and microenvironment states can be comprehensively covered.

The application discloses a kind of multi-view child tumor pathological image classification deep network incremental learning method, this method is first to the child tumor pathological image feature modeling, then under the condition of constantly obtaining new data, training obtains incremental learning model, to improve its accuracy and obtain robust classification ability.The innovation of the present application lies in the characteristics of medical pathological image are designed, a multi-level knowledge distillation regularization method is proposed to alleviate the catastrophic forgetting problem of old model in incremental learning when adapting to new data, increase the robustness and reliability of algorithm.The method of the present application can be applied to the field of medical image processing and other tasks requiring incremental learning.

The invention relates to an adrenal cortical carcinoma prognosis evaluation method based on a peripheral blood inflammation marker, which comprises the following steps: acquiring a peripheral blood detection result and tumor pathological characteristics of a patient before treatment, and calculating a derived neutrophil-lymphocyte ratio and a platelet-leukocyte ratio based on the peripheral blood detection result; inputting the derived neutrophil-lymphocyte ratio, the platelet-leukocyte ratio, the age of the patient and the tumor pathological characteristics into a prognostic scoring model to obtain a risk score; wherein the prognosis scoring model is a regression model which is screened by adopting Cox regression analysis and is established with variables related to the postoperative disease-free lifetime. According to the invention, the accuracy of prognosis evaluation of the adrenal cortex cancer can be improved.

The invention discloses a slice consistency discrimination method and system based on a wax block and a pathological slice image, and relates to the technical field of tumor pathological diagnosis. The method comprises the following steps: receiving a to-be-compared wax block image and a to-be-compared pathological section image acquired by a color camera, and using a deep learning model YOLOv11 to realize positioning of a section image guided based on the tissue size in the wax block image and a tissue area in the wax block image; constructing an image alignment module and performing training to realize alignment of the wax block image and the slice image; and inputting the aligned slice and wax block images into the trained twin-tower twin neural network for consistency discrimination. According to the method, the wax block and the section image are automatically compared and analyzed through the deep learning model, technicians are assisted to quickly complete the quality control work of the pathological section, the mode of only depending on manual sampling inspection in the past is changed, the quality control efficiency is improved, the deep learning feature extraction method is adopted to carry out image alignment and judgment, and the detection accuracy is improved. And the accuracy of judging the consistency of the wax block and the slice is improved.

The invention provides a tumor pathology image classification method based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the following steps: obtaining tumor pathology image data, and carrying out the dyeing normalization processing of the tumor pathology image data, performing multi-scale feature extraction on the processed image data by adopting a multi-branch parallel feature extraction network, and performing feature fusion to obtain a multi-scale feature map; identifying and positioning a tumor lesion area according to the multi-scale feature map, and segmenting the lesion area from the image; the segmented lesion areas are classified according to lesion types, lesion levels of the classes are analyzed after classification, and a classification level diagnosis report is output; finer grading is realized, a more accurate diagnosis basis is further provided for doctors, and the accuracy and reliability of pathological diagnosis are improved.

The invention relates to the technical field of medical image processing, discloses a quantitative evaluation method, device and equipment for a tumor pathology remission condition and a storage medium, and is used for solving the technical problems of complex calculation steps and long time consumption during quantitative evaluation of the tumor pathology remission condition in the prior art. The method comprises the following steps: acquiring a first characteristic cross section contained in a first medical image of a to-be-evaluated tumor; extracting a first feature point in the first feature section and constructing a first tumor volume model; calculating a first volume at the first target moment based on the first tumor volume model; acquiring a second characteristic cross section contained in a second medical image of the to-be-evaluated tumor; extracting a second feature point contained in the second feature section and constructing a second tumor volume model; calculating a second volume of the to-be-evaluated tumor at a second target moment based on a second tumor volume model; and calculating a pathology remission rate based on the first volume and the second volume, thereby carrying out quantitative evaluation on the tumor pathology remission condition.

The invention relates to a KIF11 and Ki67 immunohistochemical double staining kit and application, and belongs to the technical field of biological medicine. The invention provides a KIF11 and Ki67 double-staining kit for tumor pathological typing and prognosis evaluation. The kit comprises a first monoclonal antibody of an anti-KIF11 protein, a second monoclonal antibody of an anti-Ki67 protein, and a third monoclonal antibody of an anti-Ki67 protein, a second monoclonal antibody of an anti-Ki67 protein; a first species-specific second antibody corresponding to the first monoclonal antibody, wherein the second antibody is coupled with a first enzyme; a second species-specific second antibody corresponding to the second monoclonal antibody, the second antibody being coupled with a second enzyme different from the first enzyme; a first chromogenic substrate corresponding to the first enzyme, wherein the first chromogenic substrate develops a color I; and the second chromogenic substrate corresponding to the second enzyme develops a second color which can be distinguished from the first color. According to the invention, an independent prognosis index is provided by quantifying the proportion of the special subgroup KIF11-High / Ki67-High cells, and the kit has an important prognosis value.

This invention discloses a breast tumor pathology sampling device, relating to the field of medical devices. It includes an outer sampling tube and an inner sampling tube rotatably disposed within the outer tube. It also includes: a sampling mechanism for cutting and storing samples for tumor pathology; a driving mechanism for driving the inner sampling tube to rotate within the outer tube for sampling; and a negative pressure mechanism for cooperating with a sample storage chamber for tumor pathology sampling. This mechanism allows a negative pressure hose to connect to a negative pressure pump, creating a negative pressure environment within both the outer and inner sampling tubes to improve the efficiency of tumor pathology sampling. Furthermore, a storage tube can store blood from the tumor pathology sample. When the inner sampling tube absorbs the tumor pathology sample under negative pressure, the blood within the tissue is filtered and stored in the storage tube, while the tumor pathology sample is stored in the inner sampling tube. This not only efficiently and completely completes the tumor pathology sampling process but also prevents blood from affecting the tumor pathology sample.

The application discloses a kind of tumor tissue MPIF-HE image registration and cell segmentation method and system, the method includes: obtaining multiple immunofluorescence MPIF dyeing tumor tissue section image and pre-processing and multi-channel separation, obtain the single-channel MPIF image after pre-processing;Based on HE dyeing image extraction tumor infiltration front area as registration reference, carry out registration to HE dyeing image and the single-channel MPIF image after pre-processing, obtain the HE image segmentation result after registration;Based on the HE image segmentation result after registration, demarcate region of interest, and carry out segmentation and phenotype classification to the immune cell in this region of interest by pre-trained cell segmentation model, obtain cell segmentation result.The application can improve the cell segmentation precision of cell concentration and complex background.The application can be widely applied to tumor pathological image analysis technical field as a kind of tumor tissue MPIF-HE image registration and cell segmentation method and system.