11 results about "Monosomal karyotype" patented technology

The invention discloses a chromosome intelligent interpretation and karyotype analysis system and method, and relates to the technical field of chromosomes, and the system comprises an image acquisition module, a chromosome recognition module and a chromosome karyotype analysis module. According to the method, the acquisition coincidence index of each chromosome specimen is evaluated, whether the acquisition of each chromosome specimen is abnormal is judged, and the appearance identification coincidence value of each chromosome specimen is analyzed, so that the analysis error caused by poor image quality is effectively avoided, the accuracy of the chromosome appearance identification coincidence value is improved, and the reliability of the whole karyotype analysis result is improved; the probability of error analysis is reduced, the category of each chromosome is matched, the karyotype graph of the chromosome specimen is obtained, the karyotype of each chromosome specimen is analyzed, a report is generated, and feedback is performed, so that a key basis is provided for disease diagnosis and treatment, the time and energy for manually arranging the chromosomes are saved, and the working efficiency is improved. And meanwhile, the normalization and the accuracy of the karyotype graph are ensured.

The invention belongs to the technical field of karyotype analysis of chromosomes, and particularly relates to a cell pretreatment solution for karyotype analysis and preparation of chromosomes and application of the cell pretreatment solution. The cell pretreatment solution comprises the following components: 15 mmol / L Hepes buffer solution with the pH value of 7.2-7.6, 0.08-1.2 [mu] g / mL of vinblastine, 0.1-0.5 v / v% of Triton X-100 and 1-2 mmol / L of EDTA (Ethylene Diamine Tetraacetic Acid), and is a cell pretreatment solution with low toxicity, better dyeing effect and good cell type universality. When the cell pretreatment liquid is used for preparing a chromosome specimen, the preparation time is remarkably shortened, not only can more metaphase cell division phases be obtained, but also the proportion of the division phases capable of being used for chromosome analysis is greater than 83%, and the efficiency of karyotype analysis is remarkably improved. Meanwhile, the prepared chromosome specimen is clear in structure number, good in form, high in dispersity and few in background impurities, and the accuracy of chromosome karyotype analysis is remarkably improved.

The present invention belongs to the technical field of chromosome karyotype analysis, and specifically discloses a chromosome karyotype analysis method and system based on deep learning. The method includes the following steps: S1, obtaining a metaphase image of a chromosome and performing preprocessing; S2, extracting a feature F from the image; S3, clipping the feature F using multiple suggestion frames to obtain features F1 to Fn; S4, removing the common features of features F1 to Fn to obtain modified features f1 to fn, and performing classification to obtain a predicted classification result, using loss functions IG Loss and CE Loss for joint supervision; S5, fine-tuning the suggestion frame and using CE Loss to supervise the fine-tuning result; S6, using binary crossover loss (BCE Loss) to supervise the predicted classification result to obtain the chromosome karyotype analysis result. Using this technical solution, chromosome karyotype analysis is completed using deep learning and segmentation frames, and the chromosome classification effect is targetedly optimized according to the characteristics of the chromosome, thereby improving overall performance.

The utility model discloses a novel chromosome karyotype analysis platform which comprises an analysis table, a limiting groove is formed in one side of the analysis table, a limiting block is connected in the limiting groove in a sliding mode, a first connecting rod is arranged on one side of the limiting block, a first glass slide is arranged at one end of the first connecting rod, and a second glass slide is arranged at the other end of the first connecting rod. Two second connecting rods are arranged on one side of the analysis table, a second glass slide plate is arranged between the two second connecting rods, rotating shafts are arranged on the two sides of the second glass slide plate, and the rotating shafts are rotationally connected with the second connecting rods; through the sliding / rotating design of the limiting groove and the second connecting rod, the glass slide is supported to be flexibly adjusted, the chromosome banding is conveniently observed from multiple angles, the environment is kept clean in combination with the fresh air assembly of the double fans, and the sample pollution risk is reduced.

The invention belongs to the technical field of prenatal detection, and discloses application of a fetal side ventricle broadening karyotype-CNV-seq-WES stepped detection strategy in prenatal genetic counseling and clinical prognosis analysis, retrospective queue research design is adopted, 166 cases of FVM diagnosed by prenatal ultrasound are incorporated, and the detection strategy is applied to prenatal genetic counseling and clinical prognosis analysis. The system analyzes the relevance of FVM severity, anatomy types and chromosome abnormalities. The clinical application value of the genetic detection technology is evaluated by comparing genetic detection results and pregnancy outcomes of different subgroups. Through the analysis, more information is provided for the fetal medicine multidisciplinary team, so that the quasi parents are helped to obtain more comprehensive prenatal consultation and decision support.

This application provides a method and system for intelligent chromosome karyotype segmentation that integrates deep learning. The method involves: enhancing chromosome microscopic images; performing region segmentation based on the enhanced chromosome images to obtain multiple chromosome region sub-images; for any chromosome region sub-image, extracting the chromosome pixel area and band count corresponding to that sub-image, and determining the structural coverage index of the sub-image based on these values; comparing the structural coverage index with a preset chromosome banding distribution model to perform anomaly screening and identify candidate chromosome adhesion regions; and segmenting the candidate adhesion regions using a deep learning segmentation model to obtain the corresponding chromosome karyotype segmentation image. This application can screen and identify candidate adhesion regions based on the chromosome skeleton banding density, improving the accuracy of chromosome image segmentation.

The invention provides a chromosome karyotype quality evaluation system, and the system comprises a chromosome image data module which is used for obtaining and preprocessing a metaphase chromosome karyotype image, and obtaining a preprocessing result; the instance segmentation model analysis module is used for segmenting the preprocessing result and acquiring a bounding box and a mask of each chromosome; the chromosome contour extraction module is used for extracting chromosome contour features based on the bounding box and the mask; the evaluation calculation module is used for calculating an evaluation standard of image quality based on the chromosome contour features; and the comprehensive scoring module is used for normalizing each index of the evaluation standard and carrying out weighted fusion to obtain an evaluation result. According to the method, through multi-feature fusion and adaptive weight adjustment, the accuracy and robustness of chromosome image quality evaluation are remarkably improved, the workload of chromosome karyotype image screening in the clinical experiment process is greatly reduced, and the working efficiency is greatly improved.

The invention discloses a method for preparing a chromosome karyotype, which comprises the following steps: adopting a cell culture chamber glass slide, culturing antenatal villus and amniotic fluid cells on the chamber glass slide in situ, then carrying out cell hypotonic and chromosome fixing operations, dispersing chromosomes by using hot steam, uniformly dispersing chromosome suspension on the chamber glass slide, and preparing the chromosome karyotype. And a proper chromosome dispersion effect and a clear and complete karyotype are obtained. According to the method, the cell culture time is shortened, the loss of the cells is reduced to the greatest extent, the efficiency and accuracy of karyotype analysis are effectively improved, and the method has a very good application prospect.

The invention provides a chromosome karyotype searching and positioning method and system, and the method comprises the steps: scanning a chromosome sample, and collecting a microscopic image; detecting and recording a coordinate position of a chromosome karyotype according to the microscopic image; establishing a space coordinate system, and further obtaining an estimated coordinate position of the chromosome karyotype under a target objective lens; moving the target chromosome karyotype to the estimated coordinate position; grid imaging is carried out on the target chromosome karyotype, then images are matched with the target chromosome karyotypes with different multiplying powers, the actual coordinate position of the target chromosome karyotype is obtained through calculation based on the image matching result, then the deviation between the actual coordinate position and the estimated coordinate position is obtained, and the target chromosome karyotype is moved to the center of the visual field; and acquiring and storing an image of the target chromosome karyotype. According to the method, the target karyotype is placed in the central view under the high-power objective lens through position deviation correction, clear imaging is carried out, and the positioning efficiency and the positioning precision of the karyotype are improved by more than 50%.

The invention discloses a chromosome karyotype analysis method, device and equipment and a medium, and relates to the technical field of chromosome analysis, and the method comprises the following steps: carrying out genome comparison on whole exome sequencing data of a biological sample to be detected based on a preset mapping relation file to obtain a target counting matrix of a sequencing read segment, performing variation detection on the whole exome sequencing data to obtain a variation detection result file; determining sequencing read difference information and statistical significance information according to the target counting matrix and the standard counting matrix to obtain a first karyotype result; counting the number of variation sites of each chromosome of the biological sample to be detected based on the variation detection result file, and calculating allele frequency value density distribution of target chromosomes of which the number of variation sites reaches a preset variation site number threshold to analyze the target chromosomes to obtain a second karyotype result; and determining a target chromosome karyotype analysis result according to the first karyotype result and the second karyotype result. And full-exome sequencing data is directly and automatically processed.

The present invention provides a method for calculating a chromosome karyotype quality index based on deep learning technology, wherein the method comprises: obtaining a given set of chromosome karyotype images; obtaining chromosome category information corresponding to the chromosome karyotype images based on the given set of chromosome karyotype images; calculating a chromosome karyotype quality index based on the chromosome category information; and evaluating the quality of the chromosome karyotype image based on the chromosome karyotype quality index. The present invention can overcome the subjectivity and huge amount of annotation required for chromosome karyotype quality annotation and truly reflect the quality of chromosome detection and classification. It provides data support for the analysis of chromosome karyotype quality and improves the efficiency of obtaining and using chromosome karyotype quality.