14 results about "Chromosome maps" patented technology

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.

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 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.

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 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 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.

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 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 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 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.