29 results about "Chromosome maps" patented technology

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 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 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 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 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 invention discloses a chromosome image enhancement method, system and device and a medium. The method comprises the following steps: acquiring a chromosome grayscale image; extracting a foreground image of the chromosome region-of-interest; the method comprises the following steps: initializing five control points and node vectors, and generating a cubic clared B spline curve; three middle control points of the five control points are dynamic control points; constructing an image gray mapping table based on a cubic classed B spline curve; mapping the foreground image of the region of interest of the chromosome according to an image gray mapping table to obtain an enhanced chromosome image; and acquiring the real-time positions of the three dynamic control points, updating the compressed B spline curve and the image gray mapping table for three times, and updating the enhanced chromosome image. According to the method, the calculation complexity is low, the positions of the three dynamic control points can be adjusted in real time, the enhanced chromosome image can be synchronously updated so that a user can confirm the chromosome image in time, and the efficiency is improved on the premise that the enhancement quality of the chromosome image is guaranteed.

The invention relates to the technical field of chromosome image processing, in particular to a chromosome contour optimization method and device and a storage medium, and the method comprises the steps: intercepting a single chromosome image with an overlapping region from a segmented image; carrying out image background impurity removal and binaryzation to obtain a first chromosome binaryzation image; reconstructing and optimizing the first chromosome binary image to obtain a first mask image; obtaining a chromosome contour mask pattern based on the first mask pattern and the first chromosome binary pattern; and obtaining a target single chromosome image based on the chromosome contour mask pattern. The Gaussian mixture model is introduced to carry out contour optimization on the binarized image, the skeleton of the chromosome is accurately extracted, the chromosome reconstructed based on skeleton information is fused with the original contour image to obtain the target single chromosome image, the rest chromosome parts of the overlapping part and background interference are removed, and the image quality is improved. And the lines at the edges of the chromosomes are smoother and more natural, so that the final effect better conforms to the intuitive feeling of human eye recognition.

The chromosome classification processing method and system based on self-supervised contrast learning relates to the technical field of image processing; the chromosome classification processing method and system comprises obtaining a preliminary chromosome classification result; according to a self-defined homologous chromosome loss function, self-supervised contrast learning is performed on all first chromosome images and second chromosome images to obtain chromosome similarity analysis results of all first chromosome images and second chromosome images; the homologous chromosome loss function introduces a homologous similarity constraint term, and the chromosome similarity analysis results include the chromosome similarity of the first chromosome image and each second chromosome image; according to the preliminary chromosome classification result, the chromosome similarity analysis result and a priority matching rule, a final chromosome classification result is output, so as to solve the problem that the feature similarity of homologous chromosomes is not captured by the existing pre-training classification model, resulting in classification errors and affecting the accuracy of chromosome karyotype analysis.

The invention discloses a chromosome classification processing method and system based on self-supervised contrast learning, and relates to the technical field of image processing. The chromosome classification processing method comprises the following steps: acquiring a preliminary chromosome classification result; according to a self-defined homologous chromosome loss function, self-supervised contrast learning is carried out on all the first chromosome images and the second chromosome images, chromosome similarity analysis results of all the first chromosome images and the second chromosome images are obtained, homologous similarity constraint terms are introduced into the homologous chromosome loss function, and homologous chromosome similarity analysis results of all the first chromosome images and the second chromosome images are obtained; the chromosome similarity analysis result comprises chromosome similarities of the first chromosome image and the second chromosome images; and outputting a final chromosome classification result according to the preliminary chromosome classification result, the chromosome similarity analysis result and a priority matching rule so as to solve the problem that the existing pre-training classification model is insufficient in capturing the feature similarity of homologous chromosomes, resulting in classification errors and affecting the chromosome karyotype analysis accuracy.

The application provides a chromosome image analysis method and system based on progressive segmentation and focused classification, and comprises the following steps: a progressive segmentation step: performing segmentation on a metaphase chromosome image by using progressive segmentation to obtain a single chromosome image set; a focused classification step: identifying the categories of all single chromosomes in the single chromosome image set by using a focused classification algorithm, and finally generating a karyotype analysis graph. The application combines a traditional chromosome processing method and deep learning to form a progressive segmentation method, which gradually and effectively separates the adherent chromosome clusters, has high segmentation precision, and does not involve manual participation.

The invention relates to the technical field of chromosome anomaly recognition, in particular to a multimodal large model-based chromosome anomaly detection method, which comprises the following steps of: constructing an image data set and a text data set; fusing the image features and the text features to obtain multi-modal fusion features; performing abnormal scoring on the image blocks belonging to the stripe region based on an abnormal scoring rule formulated by the multi-modal fusion features, performing comprehensive processing on the abnormal scores of all the image blocks corresponding to the chromosomes, and judging whether the chromosome images are abnormal or not; and based on the multi-modal fusion feature representation and the anomaly scoring rule, decoding to generate a natural language text meeting the chromosome anomaly detection requirement. Through multi-modal fusion and a dynamic text generation mechanism, chromosome anomaly detection and strip position accurate positioning are realized, interpretable natural language text description is generated, and the practicability and interpretability of a detection result are improved.

The invention provides a chromosome tracking method and system based on targeted marking and image processing, and the method comprises the steps: obtaining an image frame sequence, carrying out the chromosome segmentation, and obtaining a plurality of pieces of first chromosome image data and corresponding chromosome segmentation masks; generating an initial three-dimensional model according to each chromosome image data and the corresponding chromosome segmentation mask; obtaining and deleting each corresponding first chromosome image data and each corresponding three-dimensional chromosome in the initial three-dimensional model according to a deletion instruction input by a user to obtain a screened three-dimensional model and a plurality of second chromosome image data; determining a moment with an optimal separation degree according to the screened three-dimensional model, and constructing a seed image frame at a corresponding moment; and according to the seed image frame, tracking and identifying each second chromosome image data after the moment with the optimal separation degree, and determining a plurality of target chromosome image data and each corresponding morphological parameter, thereby improving the accuracy and efficiency of chromosome tracking and identification.

This application belongs to the technical field of medical screening methods, specifically relating to a Transformer-based method, apparatus, device, and storage medium for Down syndrome screening. The method includes: inputting a chromosome image for screening into a preset Transformer segmentation model to segment individual chromosomes in the chromosome image, obtaining chromosome sub-blocks; the Transformer segmentation model is a Two-Stage structure model; aligning the chromosome sub-blocks to obtain individual chromosome images; inputting the individual chromosome images into a preset Transformer classification model to perform classification prediction on the individual chromosome images, obtaining predicted classification results; and performing chromosome screening based on the predicted classification results to obtain chromosome indication results. This addresses the problem of low screening efficiency.