40 results about "Chromosome maps" patented technology

The invention discloses a chromosome metaphase division phase image sorting method, which belongs to the field of image processing, and comprises the following steps of: segmenting crossed and adhered chromosomes in chromosome metaphase division phase images; extracting a plurality of features from the segmented chromosome image; setting a reasonable interval of each feature, when each feature of each image is in the corresponding interval, putting the image into a set S1, when a certain feature of each image is not in the interval, putting the image into a set S2, arranging the images in the set S1 according to the number of connected domains from large to small, arranging the images in the set S2 according to the number of chromosomes from large to small, and setting the images in the set S2 according to the number of chromosomes from large to small; the length of the set S1 is added to the serial numbers of all the images in the set S2, then the two sets are combined, overall sequencing is completed, through the method, the images meeting the requirement for karyotype analysis can be automatically screened out, and manual operation is not needed; according to the sorting method, the processing speed is high, the time required for karyotype analysis is effectively shortened, and the efficiency is improved.

The present application discloses a method, device, equipment and storage medium for constructing a population pedigree, which obtains a chromosome map file of a population sample, wherein the population sample includes multiple individual samples, and the chromosome map file includes SNP site information of the individual samples; the segment ratios of multiple target chromosome segments between individual samples are calculated based on the chromosome map file using the principle of linkage disequilibrium, and the number and type of common ancestor segments contained in each target chromosome segment are different; the genome kinship correlation coefficients between individual samples are calculated based on the chromosome map file using the similarity between SNP sites; the kinship between individual samples is determined based on the segment ratio and the genome kinship correlation coefficient; and the population pedigree of the population sample is constructed based on the genome kinship correlation coefficient and the kinship. The reconstruction of a multi-generation and multi-variety population pedigree is achieved, filling the gap in the current method for reconstructing a real large-scale population pedigree.

The invention discloses a chromosome image segmentation method based on artificial intelligence, which can solve the problems that the contour of chromosomes in a high-power lens image depends on manual annotation, the annotation workload is large, and the accuracy and consistency of manual annotation are difficult to guarantee, realizes the training of a diversified chromosome image sample set, guarantees the effectiveness of a chromosome image segmentation model, and improves the image segmentation efficiency. And the generalization ability and accuracy of the model are improved. The method comprises the following steps: acquiring a target chromosome image, and preprocessing the target chromosome image through morphological operation; acquiring contour data of overlapped chromosomes in the target chromosome image; constructing a diversified chromosome image training sample set, and training a chromosome image segmentation model by adopting a plurality of chromosome image training samples to obtain a trained chromosome image segmentation model; and inputting the preprocessed target chromosome image and the contour data of the overlapped chromosomes in the target chromosome image into the trained chromosome image segmentation model for processing to obtain a segmentation result of the overlapped chromosomes.

The invention discloses a chromosome image generation method and device for deep learning. Performing adaptive multi-scale image enhancement processing on the chromosome mid-term image to obtain an enhanced chromosome mid-term image; obtaining a chromosome karyotype arrangement diagram based on the enhanced chromosome metaphase image; generating a chromosome set based on the chromosome karyotype arrangement diagram; sequentially merging the chromosomes in the chromosome set into one image as a chromosome foreground image; fusing the chromosome foreground image with a randomly selected background image, and carrying out edge smoothing processing on the fused image to generate a random background chromosome middle-term image; wherein the random background chromosome metaphase image is used for training a target detection model established based on deep learning. According to the method, high-quality and diversified chromosome metaphase images can be generated, and when the method is used for training the deep learning model, the generalization ability and precision of the deep learning model in a target detection task are improved.

The invention discloses a chromosome karyotype analysis method and system, and relates to the technical field of image recognition, and the method comprises the following steps: S1, obtaining to-be-analyzed chromosome image data, and marking the to-be-analyzed chromosome image data to obtain a to-be-analyzed image data set; s2, introducing an improved module to perform network reconstruction on the YOLOv11 network model to obtain a chromosome detection model; s3, training a chromosome detection model through the to-be-analyzed image data set, and performing performance evaluation to obtain a target chromosome detection model; and S4, analyzing the real-time chromosome image data through the target chromosome detection model, and outputting an analysis result. According to the scheme, the detection capability of the model on chromosomes in small-target, abnormal and complex scenes is enhanced by utilizing an improved module, and the problems of low accuracy and poor robustness in the prior art are solved, so that efficient and accurate chromosome karyotype analysis is realized, and clinical and occupational health examination requirements are met.

A chromosome image segmentation identification method and system based on deep learning relates to the technical field of medical image processing, and comprises the following steps: performing target detection processing on original chromosome image data, and outputting chromosome image data calibrated with chromosome clusters; performing multi-level convolution and down-sampling processing on the chromosome image data by using a dynamic convolution kernel to obtain middle-layer down-sampling feature data and final-layer down-sampling feature data; in combination with a parameter-free attention mechanism, performing depth separable convolution processing and pixel recombination processing on the sampling feature data under the final layer to obtain deep feature optimization data; performing deconvolution processing and jump connection splicing processing on the deep feature optimization data and the intermediate layer down-sampling feature data, and outputting chromosome segmentation image data; classifying and arranging the chromosome segmentation image data to obtain a chromosome karyotype analysis result; the chromosome image segmentation and identification method and system have good segmentation and identification effects on the chromosome image.

This invention relates to the field of chromosome abnormality recognition technology, specifically to a chromosome abnormality detection method based on a multimodal large model. The method includes: constructing an image dataset and a text dataset; fusing image features and text features to obtain multimodal fusion features; assigning anomaly scores to image blocks belonging to band regions based on anomaly scoring rules formulated from the multimodal fusion features, and comprehensively processing the anomaly scores of all image blocks corresponding to the chromosome to determine whether the chromosome image is abnormal; and decoding and generating natural language text that meets the requirements of chromosome abnormality detection based on the multimodal fusion feature representation and anomaly scoring rules. This invention achieves accurate chromosome abnormality detection and band location positioning through multimodal fusion and dynamic text generation mechanisms, generating interpretable natural language text descriptions, and improving the practicality and interpretability of the detection results.

The invention provides a chromosome straightening method and device, electronic equipment and a storage medium. The chromosome straightening method comprises the steps that a chromosome image forms a binary image; s2, acquiring a central axis of each single chromosome in the binary image; s3, segmenting the chromosome image into a plurality of first-level triangles; s4, mapping the ordinates of the first-level central axis sampling points corresponding to the first-level triangles to be the same to form second-level central axis sampling points; s5, according to transformation of each first-level central axis sampling point and the corresponding second-level central axis sampling point, obtaining a corresponding second-level triangle coordinate through the first-level triangle through a rigidity-keeping image deformation algorithm; calculating an affine transformation matrix corresponding to each first-level triangle according to transformation of the first-level triangles and the corresponding second-level triangles, and performing affine transformation on the chromosome image region corresponding to each first-level triangle to obtain a straightened chromosome image; the chromosome straightening method provided by the invention can effectively correct the chromosome image.

The invention relates to the technical field of medical detection, in particular to a chromosome image detection and hardware acceleration method based on a morphological prior fusion network. According to the technical scheme, the chromosome image detection and hardware acceleration method based on the morphological prior fusion network comprises the following steps: S1, extracting brightness distribution and noise distribution of telomeres and arm regions from a chromosome medical image, and generating a normalized initial image through weighted filtering and chromaticity correction; s2, performing multi-stage segmentation on the normalized image based on medical priori, extracting telomere positions, arm length and curvature parameters in combination with a contour tracking algorithm, and generating a region label containing morphological characteristics; s3, dynamic Anchor generation: according to telomere positions, arm length and curvature parameters in the morphological characteristics, generating a candidate frame adapted to a chromosome slender curved structure. According to the method, noise interference is effectively suppressed, microstructure features such as chromosome fracture and bending are reserved, and the contour extraction precision in a complex scene is improved.

The invention discloses a deep learning-based chromosome image anomaly recognition method. The method comprises the following steps of chromosome data acquisition, image preprocessing, data enhancement, data expansion, chromosome detection network construction and training, chromosome classification network construction and training, and model reasoning. According to the method, the accuracy and robustness of chromosome detection and anomaly recognition can be effectively improved, the problem of class imbalance is relieved, the auxiliary diagnosis efficiency and reliability are improved, meanwhile, the complexity of each stage is reduced, the detection precision is improved, dependence on computing resources is reduced, and the overall performance is optimized.

The invention discloses a height-controllable chromosome image generation method and system. The method comprises the steps that a structured mask composer in a chromosome generation module obtains a chromosome skeleton line; obtaining chromosome structured data from a chromosome structured database or a chromosome structured module, wherein the chromosome structured data comprises a stripe sequence list and centromere positions; drawing stripe positions on the skeleton line according to positions in the stripe sequence list; drawing chromosome centromeres according to the positions of the centromeres to obtain a chromosome structured mask graph; the height-controllable chromosome image generation system comprises a chromosome image database, a chromosome generation module, a chromosome image structuring module and a chromosome structure database, according to the method, the chromosome image can be effectively generated, and the generation controllability is high.

The invention provides a chromosome classification method based on sequence cross-correlation. The method comprises the following steps: acquiring all chromosome images in a whole chromosome cell map; the image is filled to a multiple of 8 and then is cut into 8 * 8 image expansion data blocks, and position information is generated; splicing image extended data blocks into a sequence, and inputting the sequence into a feature encoder containing global and local self-attention modules after linear embedding and position information embedding; output features are pooled and then classified through a classification head, 24 types of probability distributions are obtained through softmax, and the highest probability index is taken as the category. According to the method, chromosome size information is reserved, the overall relation between single chromosome details and whole-graph chromosomes is considered, and generalization is better under the scenes of chromosome crossing, adhesion, slide production difference and the like.

The present application relates to a chromosome recognition method, device, computer equipment and storage medium based on deep learning. The method includes: binarizing the chromosome image to be identified to obtain a binary image of the chromosome; orthogonally projecting the binary image to determine the bending center of the chromosome, and cutting the chromosome in the chromosome image from the bending center; straightening the two parts of the chromosome obtained by cutting and splicing them according to the cut; determining a sample image based on the chromosome image to be identified and the image obtained after splicing; performing deep learning training based on the sample image to obtain a chromosome classification model; identifying the category of the chromosome in the chromosome image to be identified through the chromosome classification model. This solution can save costs.

The application provides a kind of micro nuclear blood cell identification system based on generative model data augmentation, adopts micro nuclear identification model, and the blood cell image in chromosome image database is input into generative model offline training module to complete distributed training respectively, and the image of specified cytoplasm and nucleus position and contour is generated;Wherein, the generative model offline training module trains the diffusion probability model based on control network, which is used to train the model that can generate a large number of negative and positive samples, and the negative micro nuclear and positive micro nuclear cell image generation module is used to generate a large number of negative and positive images by the model trained by the generative model offline training module, then the negative and positive images obtained are classified by the micro nuclear identification model training module, to obtain the binary classification result of negative and positive, and the probability value of micro nuclear cell is displayed through user interface.The application can quickly obtain more representative data set, and provide more accurate support for medical diagnosis and disease treatment.

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 is suitable for the technical field of image processing and biomedical analysis, and provides an atomic force microscope chromosome image fusion and detection method and system, and the system comprises an image loading module which is used for loading a chromosome image file scanned by an atomic force microscope from a specified folder; and the image splicing module is used for splicing the loaded image files into a panoramic image and comprises an automatic splicing sub-module and a manual splicing sub-module. According to the method, automatic splicing and intelligent detection of chromosome images can be realized, the stability and accuracy of image processing are greatly improved, high-quality panoramic image construction can still be realized in a complex scene, the image utilization rate is improved, a chromosome extraction result is more accurate and reliable, the whole scheme is high in automation degree and strong in robustness, manual intervention can be reduced, and the efficiency is improved. The method improves the processing efficiency and detection accuracy of the chromosome image of the atomic force microscope, and has a remarkable practical value.

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 present invention discloses a chromosome recognition method and system. The method comprises obtaining chromosome data; training a diffusion probability model according to the chromosome data; embedding a chromosome mask binary image and a chromosome grayscale normalized histogram of the chromosome image and a chromosome category into a feature vector, using a cross-attention mechanism to apply the feature vector to the diffusion probability model to generate chromosome expansion data; training a chromosome recognition network according to the chromosome expansion data; and identifying the chromosome data to be identified according to the chromosome recognition network; the data amplification method based on the diffusion probability model of the present invention not only retains the authenticity of the chromosome, but also successfully converts the image characteristics of the chromosome, and effectively expands the diversity of the data. Through this system, the recognition effect of chromosomes on different data sources has been significantly improved, providing a more accurate and reliable auxiliary tool for the field of medical diagnosis.

The application provides a template image-based bone marrow abnormal chromosome detection method, device and medium. The method comprises: acquiring a bone marrow chromosome image to be detected; determining a target category of chromosomes in the bone marrow chromosome image through a trained and tested classification model; selecting a template image corresponding to the target category from a template library, wherein the template library stores a plurality of images representing normal bone marrow chromosomes; inputting the template image and the bone marrow chromosome image into a trained and tested difference analysis model to obtain a detection result, wherein the detection result comprises identification information representing whether the chromosomes in the bone marrow chromosome image are normal or abnormal, and an abnormal type corresponding to the abnormality when the chromosomes are abnormal. In this way, the accuracy of chromosome abnormality detection can be improved.

The application relates to a chromosome image segmentation and recognition method and system based on deep learning, and relates to the technical field of medical image processing.The method comprises the following steps: performing target detection processing on original chromosome image data to output chromosome image data with labeled chromosome clusters; using a dynamic convolution kernel and performing multi-level convolution and down-sampling processing on the chromosome image data to obtain intermediate layer down-sampling feature data and final layer down-sampling feature data; combining a parameter-free attention mechanism, performing deep separable convolution processing and pixel recombination processing on the final layer down-sampling feature data to obtain deep layer feature optimization data; performing inverse convolution processing and jump connection splicing processing on the deep layer feature optimization data and the intermediate layer down-sampling feature data to output chromosome segmentation image data; and performing classification and arrangement processing on the chromosome segmentation image data to obtain a chromosome karyotype analysis result.The chromosome image segmentation and recognition method and system can achieve good segmentation and recognition effects on chromosome images.

The invention provides a chromosome structure anomaly identification method and device, and a storage medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining a chromosome grayscale image, and extracting a central axis of a chromosome from the chromosome grayscale image through a deep learning model; based on the central axis, performing expansion processing on the chromosome image, and generating a two-dimensional expansion image representing strip gray level distribution; analyzing the two-dimensional expanded image to obtain a one-dimensional strip gray scale signal; identifying the definition grade of the chromosome according to the one-dimensional strip gray scale signal; and on the basis of the definition level, extracting the feature of the one-dimensional stripe gray signal, comparing the feature with the feature of the standard chromosome stripe of the corresponding definition level, and judging whether the structure is abnormal or not according to the comparison result. The method can accurately obtain the gray scale distribution of the strip without being influenced by the bending form of the chromosome, achieves the one-dimensional representation of the strip, lays a foundation for the subsequent automatic analysis, and can recognize the unknown structure abnormality under the condition that only a normal chromosome sample is used.

The invention provides a preparation method of lycoris plant root tip chromosomes, and belongs to the technical field of biology. The preparation method of the lycoris plant root tip chromosome comprises the steps of root tip induction culture, root tip collection and pretreatment, enzymolysis treatment, chromosome flaking, microscopic observation and the like. A normal-temperature and low-temperature combined 8-hydroxyquinoline double-stage pretreatment mode is adopted for newborn root tips, and a compound enzymatic hydrolysate and a buffer solution pre-preservation system with an optimized ratio are combined, so that the metaphase division cell proportion and the chromosome image quality are effectively improved. The method has the advantages of simplicity and convenience in operation, high efficiency, strong repeatability and the like, is suitable for preparing chromosomes of different types of lycoris radiate and hybrid materials thereof, and can also be popularized and applied to cytogenetics research of other bulb ornamental plants.

The application discloses a chromosome image generation method and device for deep learning. Adaptive multi-scale image enhancement processing is performed on a chromosome metaphase image to obtain an enhanced chromosome metaphase image; a chromosome karyotype arrangement map is obtained based on the enhanced chromosome metaphase image; a chromosome set is generated based on the chromosome karyotype arrangement map; chromosomes in the chromosome set are sequentially merged into one image as a chromosome foreground image; the chromosome foreground image is fused with a randomly selected background image, and edge smoothing processing is performed on the fused image to generate a random background chromosome metaphase image; wherein the random background chromosome metaphase image is used for training a target detection model established based on deep learning. The method can generate high-quality and diversified chromosome metaphase images, and thus is used for training a deep learning model, and improves the generalization ability and precision of the deep learning model in a target detection task.

The invention provides a chromosome structure anomaly detection method and device, and solves the problems of insufficient local feature representation of chromosomes, neglect of an inherent sequential relationship in a chromosome image and limited detection range in the prior art. Comprising the following steps: S1, acquiring image data of chromosomes; s2, carrying out segmentation processing on the image data of the chromosomes to form image blocks; s3, encoding the image blocks according to a sequence form to form context representation of the image blocks; and S4, detecting whether the chromosome is abnormal or not according to the context representation of the image block, thereby realizing efficient and accurate detection of the local abnormality of the chromosome structure.

This application relates to a chromosome image processing method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: acquiring an input image containing multiple chromosomes in metaphase; extracting individual chromosome images from the input image; performing feature analysis on each chromosome based on its image to obtain multidimensional chromosome feature information; and determining the phase quality assessment result of the input image based on statistical information corresponding to the multidimensional chromosome feature information. This method can improve the accuracy of chromosome image processing.

The invention provides a ViT model-based chromosome image processing general model and an application method thereof, the ViT model-based chromosome image processing general model and the application method thereof, the used general model comprises an embedded layer, a multi-layer Transform coding layer and a multi-layer sensing machine head, the multi-layer Transform coding layer is an encoder of a pre-trained MAE model; wherein the pre-trained encoder can automatically extract useful features from a large amount of chromosome data which is randomly shielded, so that the dependence on large-scale labeled data is reduced, and the accuracy of an image processing model and the training efficiency of the model are effectively improved; meanwhile, when the encoder network is trained, input data is partially shielded and then recovered by a decoder, so that important information in the chromosome sequence can be efficiently captured, the robustness and generalization ability of the encoder network can be enhanced, and when the long and complex chromosome sequence is processed, the method has the advantages that the robustness and generalization ability of the encoder network are improved, and the robustness and generalization ability of the encoder network are improved. And the problem of data over-fitting can be reduced.

The present invention discloses a highly controllable chromosome image generation method and system, the method comprising: a structured mask constructor in a chromosome generation module obtaining a chromosome skeleton line; obtaining chromosome structured data from a chromosome structured database or a chromosome structured module, the chromosome structured data comprising a band sequence list and a centromere position; drawing the band position on the skeleton line according to the position in the band sequence list; drawing the chromosome centromere according to the centromere position to obtain a chromosome structured mask diagram; the highly controllable chromosome image generation system comprises: a chromosome image database, a chromosome generation module, a chromosome image structuring module, and a chromosome structure database; the method can effectively generate chromosome images, and the generation is highly controllable.

The invention discloses a metaphase division cell optimization method and system for chromosome aberration detection, and relates to the technical field of information processing, and the method comprises the following steps: S1, carrying out the grading labeling of an obtained metaphase division chromosome image, and constructing an image grading data set; s2, training the artificial intelligence model through the image grading data set to obtain a preliminary screening grading model; s3, grading and screening the real-time chromosome image data through the preliminary screening and grading model to obtain a to-be-selected image data set; s4, extracting morphological features and corresponding weight parameters of each chromosome image in the to-be-selected image data set to construct a basic scoring function; s5, the basic scoring function is combined with an AI confidence coefficient weighting mechanism to determine a scoring sequence of each level of image; and arranging the scoring sequences in a descending order and outputting target images in sequence. The problems of low screening efficiency, poor result consistency and one-sided evaluation dimension in the prior art are solved, and the stability and reliability of the screening result are improved.

The present invention discloses a probe group preparation method, a probe group sequence and a map construction method for constructing a single chromosome map of a species with a high repetitive sequence and a large genome, and belongs to the field of genetic engineering technology. The probe sequence is all sequences or their reverse complementary sequences contained in the OMIX database https: / / ngdc.cncb.ac.cn / omix / with the accession number OMIX006067. The probe group and method disclosed in the present invention have high density and long interval, and achieve the effect of clear signal visualization, which can be applied to the analysis of plant chromosome structural variation and auxiliary mining of planting resources.

The invention discloses a method for automatically generating a cross chromosome image and a computer program product. The method comprises the following steps: S3, extracting a reference chromosome from a reference chromosome image; and S4, the extracted reference chromosomes are moved to the to-be-synthesized chromosome image and are intersected and overlapped with the to-be-synthesized chromosomes in the image for synthesis, and the intersected and overlapped reference chromosomes and the to-be-synthesized chromosomes are not the same chromosome. According to the method, a reference chromosome is extracted from a reference chromosome image in the step S3, and then the reference chromosome is put into a to-be-synthesized chromosome image to be subjected to cross synthesis with the to-be-synthesized chromosome in the step S4, so that a cross chromosome image is obtained. When the method is used for many times, different chromosomes to be synthesized and reference chromosomes are selected for each time, and a large number of chromosome image samples with different chromosome crossing conditions can be generated for an artificial intelligence algorithm model to carry out chromosome crossing recognition analysis training.