15 results about "Marrow cell" patented technology

The invention belongs to the technical field of medical image processing, and provides a bone marrow cell WSI region-of-interest automatic analysis positioning method and system, and the method comprises the steps: obtaining a full glass slide bone marrow aspiration smear image (WSI); a pyramid network is adopted to zoom the full-glass-slide bone marrow aspiration smear image, and a low-resolution image is generated; designing a general area condition prompt strategy by combining bone marrow cytology medical priori knowledge, and calling a language model to construct text description of a bone marrow microparticle area for the low-resolution image; using a visual language model to align the text and the low-resolution image, and then adopting a feature visualization technology and based on gradient weighted channel selection to generate a semantic response graph; the dispersion degree of a semantic response region in the semantic response graph is calculated, and segmentation prompt information is generated by adopting a region perception and strategy self-adaption mechanism; and inputting segmentation prompt information and the low-resolution image into an SAM model, generating an ROI mask image through reasoning, and completing positioning of the bone marrow cell WSI region of interest.

In the field of gene therapy, a major hurdle is the design and identification of constructs and gene therapy vectors providing therapeutic effects while displaying satisfactory safety profiles. In the treatment of Diamond-Blackfan Anemia (DBA), therapies alleviating several crucial anemia symptoms, such as blood or bone marrow cellularity, hemoglobin levels, erythrocytes levels, or platelet levels, while showing satisfactory safety profiles remain a challenge. The present invention provides constructs encoding ribosomal protein genes involved in DBA, such as genes encoding RPS19, RPS17, RPS24, RPS10, RPL35a, RPL11, RPS26, and RPL5, vectors, methods, cells, and medical uses thereof, addressing these challenges and finding particular applications in the field of autologous cell therapy treatment of DBA. Further, the present invention provides a non-genotoxic conditioning protocol for preparing a subject prior to cell therapy treatment for DBA using construct of the present invention.

The invention specifically discloses a bone marrow blood cell target identification and counting method based on an improved single stage, and relates to the technical field of medical image processing. The method comprises the following steps: S1, image acquisition and data preparation; s2, constructing and preprocessing a data set, and dividing the preprocessed data set into a training set, a verification set and a test set; s3, constructing an initial model of an improved single-stage target recognition model; s4, training the initial model by using a training set and a verification set to obtain a bone marrow blood cell target recognition and counting model; and S5, identifying and positioning the bone marrow blood cells in the test set image by adopting a bone marrow blood cell target identification and counting model, outputting cell categories and target frame coordinates, and realizing counting according to the number of target frames. According to the method, the detection problem caused by large bone marrow cell scale change is solved, the classification precision is improved, and unification of high precision and high speed of bone marrow blood cell target identification and counting is realized.

The application discloses a bone marrow cell recognition result merging and counting method and system based on a double-space ontology constraint model. The method acquires a single cell record set, loads a statistical rule model containing a fine-grained label space, a clinical statistical space, a merging relationship, a denominator caliber relationship, an abnormal isolation relationship and a rule version identifier, forms a conversion path constrained by the rule version identifier, after rule integrity verification, maps an original fine-grained category to a clinical statistical node, determines a denominator caliber type and divides a denominator set, and generates an independent abnormal statistical item record; generates a clinical statistical result data set according to the clinical statistical node, the denominator set and the independent abnormal statistical item, and forms a reversible traceable data set. The application can reduce statistical denominator mixing, retain abnormal morphological characteristics, support rule version traceability, model upgrade compatibility and artificial correction incremental recalculation.

The application discloses a kind of bone marrow cell few sample classification method, system and electronic equipment, method includes: obtaining bone marrow cell dataset, constructs few sample training set and test set establishes the multi-scale multi-modal few sample classification network model based on text multi-source generation selection;According to the few sample training set, the few sample classification network model is trained, and the trained bone marrow cell classification network model is obtained;The bone marrow cell image of test is input into the bone marrow cell classification network model, and the prediction class of bone marrow cell image is obtained, realizes the bone marrow cell image classification under the condition of few sample training.The present application selects histopathology pre-training model for bone marrow cell classification, and designs text multi-source generation selection module, standardizes the semantic description of each category of bone marrow cell, and further improves the few sample classification accuracy of bone marrow cell, which can be widely applied in the field of computer vision technology.

The application discloses a kind of based on improved single-stage marrow blood cell target identification and counting method, it is related to medical image processing technical field.The method steps are:S1, image acquisition and data preparation;S2, the construction and pre-processing of data set, and the data set after pre-processing is divided into training set, verification set and test set;S3, the initial model of improved single-stage target identification model is constructed;S4, the initial model is trained using training set and verification set, and marrow blood cell target identification and counting model is obtained;S5, marrow blood cell target identification and counting model is used to identify and locate marrow blood cell in test set image, output cell class and target frame coordinates, and counting is realized according to target frame number.The application solves the detection problem caused by the large scale variation of marrow cell, improves the classification precision, realizes the unification of high precision and high speed of marrow blood cell target identification and counting.

The invention discloses a bone marrow cell morphology identification method and system based on a cross-domain adaptive joint convolutional neural network, and relates to the technical field of computer-aided diagnosis, the method comprises the following steps: S1, obtaining bone marrow cell image data, and carrying out image segmentation through a dual-path mask optimization segmentation module to obtain a single cell segmentation image; s2, extracting and fusing pathological features of the single cell segmentation image through a four-dimensional feature fusion module to obtain a spatial fusion feature vector; s3, performing feature alignment on the spatial fusion feature vector through a cross-domain adaptive joint module to obtain an aligned domain invariant feature vector; and S4, outputting a cellular morphology recognition type from the multi-center feature distribution vector through a dynamic element classifier. According to the method, high-precision and high-robustness automatic recognition of the bone marrow cellular morphology in a high inter-domain difference environment is realized, and the method has a significant clinical application value.

This utility model relates to a semi-automatic bone marrow cell morphology slide washing instrument, comprising: a washing chamber with multiple slots for holding slides; and a robotic arm equipped with a washing head for driving the washing head to move. By holding the slides in the slots and using the robotic arm to drive the washing head, the water flow and angle can be controlled, reducing cell detachment. The closed washing chamber design allows the operator to have zero contact with the staining solution, improving operational safety. Multiple slides can be processed at a time, increasing washing efficiency.

The invention discloses a bone marrow cell classification method, system and device based on semi-supervised domain adaptation and a medium, the method comprises the steps that a data set is acquired and processed, and the data set comprises a source domain data set and a target domain data set; establishing a domain adaptive classification network model based on high-frequency feature fusion and target domain distribution disturbance alignment, wherein the domain adaptive classification network model adopts Predamp; the system is based on a Guide skeleton and comprises a high-frequency feature fusion module and a target domain distribution disturbance alignment module. Training the domain adaptive classification network model by using the source domain data set and the target domain data set to obtain a trained domain adaptive classification network model; and inputting the bone marrow cell image of the target domain data set into the trained domain adaptive classification network model to obtain a prediction category of the bone marrow cell image so as to realize semi-supervised bone marrow cell image classification. According to the method, more accurate label propagation of the target domain can be improved, so that the bone marrow cell image classification accuracy of the target domain is improved.

This invention describes new methods for the mobilization of human cells such as hematopoietic stem cells in bone marrow, and the subsequent collection of such cells from the peripheral blood of deceased donors. These methods produce quantities of hematopoietic stem cells that can be used in a multitude of possible applications without harming the deceased donor or the recovery of the deceased donor's organs for solid organ transplantation. The methods involve the administration of one or more mobilizing agents to mobilize bone marrow cells into the peripheral blood cells followed by the collection of cells via apheresis.

The application discloses a kind of based on improved ResNet-50 network's bone marrow cell classification method, belong to the medical auxiliary diagnosis field of artificial intelligence.The method includes: by designing a new classification model of fusion multiscale feature, introduce PSA module to improve ResNet-50 network, introduce CSAB module to enhance the edge feature extraction capability to image, help model capture the local details and global structure information of bone marrow cell image;Introduce ACAM module to improve the attention of model to different categories of bone marrow cell key features;By training the improved ResNet-50 network, the optimal model obtained by training is classified.In improving classification efficiency while ensuring high accuracy, compared with other bone marrow cell classification models applied has better performance.

The utility model discloses a multifunctional culture shelf for a bone marrow chromosome culture medium, which belongs to the technical field of bone marrow chromosome culture, and is characterized by comprising a box body, upright posts are fixedly connected to four corners in the box body, and a plurality of placing components are movably connected between opposite sides of the upright posts on two sides; the rear side of the interior of the box body is fixedly connected with an adjusting assembly, the adjusting assembly comprises a main plate, and the front side of the main plate is fixedly connected with a plurality of ultraviolet germicidal lamps. The problems that the temperature of a culture environment is inconvenient to adjust, and too high or too low temperature possibly affects the activity of bone marrow cells and hinders normal replication and separation of chromosomes, so that culture failure or chromosome abnormality is finally caused, culture of bone marrow chromosomes is affected, and the practicability of the device is reduced are solved.

This application provides a method, system, device, medium, and program product for analyzing bone marrow metastases in neuroblastoma, relating to the fields of intelligent medical image analysis and artificial intelligence-assisted diagnosis. The application includes: obtaining digitized whole-slice images of bone marrow cells from bone marrow cytological smears and preprocessing these images; preprocessing the whole-slice images to obtain bone marrow cell image blocks; weighting the bone marrow cell image blocks to obtain bone marrow cell feature images; wherein, the weighting processing includes feature extraction from the bone marrow cell image blocks to obtain corresponding feature vectors, employing multi-instance learning to represent the feature vectors at the whole-slice level to obtain bone marrow cell feature images; and classifying the bone marrow cell feature images to obtain corresponding classification results. This application achieves effective fusion from local cytological features to whole-slice-level diagnostic results through a multi-instance learning aggregation module.

The invention relates to a bone marrow cell image sample generation method and device, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining a simulated background / red blood cell segmentation binary image randomly generated by a generative adversarial network, and inputting the simulated background / red blood cell segmentation binary image as a constraint condition into a conditional generative adversarial network; acquiring a simulated background / red blood cell RGB image generated by the conditional generative adversarial network; obtaining a simulated karyocyte probability distribution function generated by a variational auto-encoder; performing random sampling on an image corresponding to the simulated nucleated cell probability distribution function according to a preset number, and determining coordinate positions of nucleated cells in a bone marrow cell image sample to be generated; and pasting the nucleated cell cut image in the bone marrow cell smear image to the coordinate position corresponding to the simulated background / red blood cell RGB image to generate a bone marrow cell image sample, thereby effectively improving the bone marrow cell image sample generation efficiency.