12 results about "Blood smear" patented technology

The present invention relates to a computer-implemented method for producing a trained model for detecting one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from a sample of a body liquid, bone marrow, cytology smear or slide preparations, in particular a blood smear sample. It relates also to a computer-implemented method for detecting one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from a sample of a body liquid, bone marrow or cytology smear, in particular a blood smear sample.

This invention relates to a sialylated glycoprotein detection reagent, its preparation method, and its application, belonging to the field of biodetection technology. The detection reagent uses purified water as a solvent and comprises the following components: surface-modified titanium dioxide nanomaterials, metal ions, a pH adjuster, and a solvent. The surface-modified titanium dioxide nanomaterials are obtained by dispersing titanium dioxide nanoparticles in an organic solvent and reacting them with a silane coupling agent. The pH value of the sialylated glycoprotein detection reagent is ≥ 8.0. The reagent of this invention utilizes the high affinity of modified titanium dioxide nanomaterials for sialic acid to specifically aggregate sialylated glycoproteins in blood into particles, facilitating observation. This reagent exhibits good stability, high sensitivity, and strong specificity, making it suitable for rapid tumor screening of blood smear samples.

The present invention pertains to the computer implemented method for identifying a microorganism in a blood comprising: receiving (101) a microscopic image of a blood smear; detecting (102) plurality of microorganism cells in the microscopic image using a deep learning cellular segmentation model; extracting (103) from the segmented microscopic image a patch of size HxW centred around a centroid of the detected microorganism cell for each detected microorganism cell; encoding (104) of each patch by a deep neural network encoding model into a patch representation vector u n of size 1×D; aggregating (105) the patch representation vectors un using a pooling method to generate a sample representation vector u of size 1×D; and indicating (106) a species of microorganism present in the microscopic image by classifying the sample representation vector u by a neural network classifier to class representing specific species. In another aspect the invention relates to the data processing system for carrying out the steps of the method according to the invention.

According to the automatic detection method and device for the blood cells in the blood smear image, a local teacher model and an external teacher model are combined to generate high-quality pseudo labels for original extremely sparse labeled data, the models are guided to learn a complete foreground concept, and the problem of a large amount of missing detection caused by insufficient labeling is effectively relieved; the text feature of the blood cell category name is used as a domain invariant anchor point, a detector is guided to generate visual representation with strong generalization, and excellent generalization performance is achieved in multiple visual domains; inter-text comparative learning and inter-visual comparative learning are introduced to promote clearer alignment between text features and visual features from multiple dimensions, and generalization is improved while discrimination is guaranteed.

The application discloses a blood smear white blood cell classification counting method and system based on image analysis, and belongs to the technical field of medical image processing. The method comprises the following steps: after collecting a peripheral blood smear image, performing adaptive background correction and color standardization pretreatment; detecting and segmenting each white blood cell through an instance segmentation network; extracting three-dimensional features of nuclear morphology, cytoplasmic staining and nuclear-cytoplasmic ratio and adaptively fusing; adopting a cascade classifier to perform five-class white blood cell classification and abnormal morphology identification; and counting and labeling suspected abnormal cells for review. The classification coincidence rate of the application reaches more than 95%, can effectively identify abnormal morphologies such as atypical lymphocytes, immature cells and toxic granules, and provides technical support for blood tests at the grassroots level.

The present invention relates to a computer-implemented method for producing a trained model for detecting one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from a sample of a body liquid, bone marrow, cytology smear or slide preparations, in particular a blood smear sample. It relates also to a computer-implemented method for classifying a sample of a body liquid, bone marrow or cytology smear, in particular a blood smear sample, as indicative or non-indicative of one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from the sample.

1. The name of the design product: pusher. 2. The use of the design product: the design is used for pushing blood smear or other body fluid smear. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view.

This invention provides a deep learning-based system for evaluating abnormal erythrocyte morphology in whole-slide blood smears. The system includes a dual classification module, a fitting and comparison module, and an evaluation output module. It acquires whole-slide images of blood smears containing various abnormal erythrocyte morphological features, classifies and labels the images, constructs an abnormal erythrocyte database, and uses a deep convolutional neural network combined with a multi-feature extraction algorithm to achieve dual classification of erythrocytes. The dual classification results are then fitted and compared to generate a comprehensive evaluation report of abnormal erythrocyte morphology in the whole-slide blood smear. The innovation of this invention lies in constructing a complete deep learning detection workflow. Through deep learning and multi-feature fusion, it significantly improves the accuracy and interpretability of abnormal erythrocyte identification, achieving accurate evaluation of abnormal erythrocyte classification at the whole-slide scale, and providing objective quantitative evaluation basis for clinical diagnosis.

This invention relates to an automated smear staining device and method for using avian blood samples. The smear staining device includes a base, a working platform, a slide positioning structure, a screw-slide mechanism, a slide pushing component, a heating component, an ultrasonic ceramic transducer, a rotary dispensing component, a staining solution bottle, a rotary servo motor, a micro peristaltic pump, and a control system. The slide pushing component holds a standard glass slide as the slide pusher and, driven by the screw-slide mechanism, completes the spreading and film formation of the blood sample. The rotary dispensing component, driven by the staining rotary servo motor, rotates from a standby position to a working position to quantitatively add staining solution to the slide. The heating component is used for the initial drying of the blood smear and the subsequent drying after staining. The ultrasonic ceramic transducer assists in the spreading of the staining solution. This invention can improve the consistency and continuity of on-site smear preparation and staining of avian blood samples.

This application proposes an automatic detection method and device for blood cells in blood smear images. It combines a local teacher model and an external teacher model to generate high-quality pseudo-labels for the originally extremely sparse labeled data, guiding the model to learn complete foreground concepts and effectively alleviating the problem of a large number of missed detections caused by insufficient labeling. It uses the text features of blood cell category names as domain-invariant anchors to guide the detector to generate visual representations with strong generalization, achieving excellent generalization performance in multiple visual domains. It introduces inter-text contrastive learning and inter-visual contrastive learning to promote clearer alignment between text features and visual features from multiple dimensions, improving generalization while ensuring discriminability.

The present invention relates to a computer-implemented method for detecting one or more defined genomic aberrations from images of a body fluid, bone marrow or cytological sample, such as a cytological smear sample, in particular a blood smear sample.

The utility model belongs to medical examination technical field, concretely is blood smear integration device of dyeing, including inspection box, the extension frame is fixedly connected with the top of inspection box, one pair of guide rails is fixedly connected with extension frame side wall, the end of guide rail is fixedly connected with baffle, is equipped with dyeing subassembly on the guide rail, dyeing subassembly is equipped with multiple sets, dyeing subassembly includes fixed plate, sliding slot, object carrier, clamping tooth, the sliding slot is seted up in fixed plate side wall, the sliding slot is correspondingly set up with guide rail, is equipped with rotating subassembly on fixed plate, object carrier is installed in fixed plate inboard, through above -mentioned structure, setting inspection box, extension frame, guide rail, baffle and dyeing subassembly, can be convenient for fixing to multiple sets of slide simultaneously, to improve the operation efficiency when dyeing, the interval between the clamping tooth of multiple sets of dyeing subassembly is different simultaneously, and dyeing subassembly and inspection box between sliding fit, can adapt to different size's slide dyeing, to improve the applicability of overall device.