40 results about "Nuclei segmentation" patented technology

The invention discloses a weak supervision cell nucleus segmentation method based on wavelet differential convolution and region expansion, and relates to the technical field of image processing, and the method comprises the following steps: obtaining an image sample containing a cell nucleus, and marking the position of the cell nucleus through central point annotation; a wavelet differential convolution module is designed, multi-scale features are extracted through discrete wavelet transform, and cell nucleus boundary and detail information are enhanced in combination with the differential convolution module; a region expansion module is constructed, pseudo labels are generated based on point annotation iteration, a complete cell nucleus region is expanded step by step, and the problems of noise and nucleus missing detection are reduced; building a segmentation network, adding a wavelet difference convolution module to extract detail features, and optimizing network performance by using a pseudo tag as a weak supervision signal; carrying out segmentation prediction, and outputting the accurate position and shape of the cell nucleus; therefore, high-precision segmentation is realized under a small amount of annotation information, the annotation dependence is reduced, and particularly, the effects of reducing adjacent cell nucleus boundary adhesion and small cell nucleus leak detection are remarkable.

The invention provides a method and system for interpreting expression of ER and PR in breast cancer by using artificial intelligence, and relates to the technical field of artificial intelligence, the method comprises the following steps: collecting labeled breast cancer tissue pathological section images, and performing data enhancement to obtain a sample image set; performing chromatic aberration balance on the image data in the sample image set to obtain a preprocessed data set; training a preset initial segmentation network by using the preprocessed data set; inputting the breast cancer tissue pathological section image into the trained cell nucleus segmentation model to obtain a cell nucleus region image; performing morphological processing on the cell nucleus region image to obtain a processed target region; and extracting color features and morphological features in the target area, and inputting the color features and the morphological features into the classification model to obtain an interpretation result. According to the method, cell nucleus segmentation and feature extraction are carried out through the deep learning model, subjectivity and errors of manual interpretation are reduced, and the interpretation accuracy is improved.

The invention discloses a pathological image full-automatic segmentation and classification method and system based on deep learning, and the method comprises the steps: selecting a to-be-processed tissue slice image, extracting the feature information of a cell nucleus in the to-be-processed tissue slice image, and carrying out the initialization processing; the unified feature decoder is introduced to process the image, and different feature information is integrated; carrying out instance segmentation on the cell nucleus by adopting a convolutional neural network, and optimizing a feature extraction process in combination with a self-attention mechanism and a residual network; meanwhile, a classification module is used for carrying out category prediction on cell nucleuses, and tissue region guide information is fused; and carrying out comprehensive optimization on the segmentation result and the classification result through multi-task joint training until a preset stop condition is met, and obtaining a high-quality cell nucleus segmentation and classification result. The method can automatically and efficiently perform cell nucleus segmentation and classification in the digital pathological image, has high accuracy and stability, is suitable for various complicated tissue slice analysis tasks, and does not need manual intervention.

The application discloses a method, system, device and medium for recognizing point fluorescence signals in cell nuclei. The method comprises inputting a pre-processed tissue slice scanning image into a cell nucleus segmentation network model to obtain a binary segmentation mask image; determining a target cell nucleus mask based on the binary segmentation mask image; multiplying a point fluorescence signal channel with the target cell nucleus mask to obtain a multiplication feature result image; inputting the multiplication feature result image into a point fluorescence signal recognition model to obtain a signal point heat map and a preliminary segmentation mask; fusing the signal point heat map, the preliminary segmentation mask and the point fluorescence signal channel to obtain a fusion feature image; inputting the fusion feature image into a signal point instance segmentation model to obtain a plurality of single signal point instances; and determining a target ACD score of the tissue slice scanning image based on the plurality of single signal point instances. The application can improve the accuracy, stability and efficiency of recognizing point fluorescence signals in cell nuclei.

The present invention discloses a medical image segmentation model based on pseudo-mask-guided feature aggregation. The medical image segmentation model includes: a convolutional block, a second-order network model structure, a pseudo-mask-guided feature enhancement module, a multi-scale multi-stage feature aggregation module, a first convolutional layer, a second convolutional layer, a third convolutional layer, and a loss function. The second-order network model structure includes: a first-order sub-network and a second-order sub-network. The medical image segmentation model of the present invention is tested on the nucleus segmentation MoNuSeg dataset and the gland segmentation CRAG dataset using multi-scale and multi-stage features, not only extracting detailed nucleus and gland segmentation results, but also achieving an advanced quality assessment effect.

Examples herein include methods, systems, and computer program products for utilizing neural networks in ultrasound systems. The methods include processor(s) of a computing device obtaining an image that depicts cells. The processor(s) applies one or more nuclei detection algorithms to detect nuclear aspects in the image. The processor(s) generates a nuclear segmentation map. The processor(s) utilizes the nuclear segmentation map to identify one or more regions of interest in the image. The processor(s) generates a classification result by automatically determining a cell type for each cell in a region of interest of the regions of interest.

This application discloses a cell nucleus segmentation method based on region enhancement, belonging to the field of intelligent pathological diagnosis. The method includes preparing a training data set to obtain a cell nucleus image; building a cell nucleus segmentation network model, loading the cell nucleus image, and obtaining highlighted salient features, wherein the cell nucleus segmentation network model includes a contour branch for detecting the cell nucleus edge and generating contour features, a coarse segmentation branch for completing the semantic segmentation task of the nucleus and combining the contour branch to predict the cell nucleus proposed region, and a fine segmentation branch for processing the image after region enhancement; automatic recognition operations are performed based on the salient features of the cell nucleus segmentation network model; training and testing the cell nucleus segmentation network model to segment the cell nucleus. This application can accurately segment the boundaries of the cell nucleus.

The invention discloses a pathological image segmentation method and system based on coevolution generation type difficult sample mining, and the method comprises the steps: constructing a mask synthesis engine guided by biological information, and generating a cell nucleus mask through introducing a cell affinity matrix and structure prior based on a graph theory; establishing a segmentation-oriented adversarial renderer, and aligning the generated image with a real image in a feature space by using a multi-layer feature discriminator; implementing a closed-loop co-evolution strategy, dynamically identifying vulnerability categories by using performance feedback of the segmentation model, and guiding a generator to carry out adaptive difficult sample mining; and obtaining a cell nucleus segmentation result through alternate mutual promotion of the generator and the segmentation model and regression fine tuning of real data. According to the method, the problems that in existing small sample learning, generated data lacks biological rationality and visual fidelity cannot be converted into segmentation performance are solved, and the segmentation precision and generalization ability of the model are remarkably improved under the condition of extremely few labeled data.

The present invention relates to a cell analysis system based on virtual fluorescence generation technology, and a method therefor. According to the present invention, the system comprises: an input unit that receives a quantitative phase image of a sample including at least one cell through digital holography microscopy; and a cell identification unit that identifies at least one living cell by applying, to the received quantitative phase image, a cell segmentation mask generation model and a nuclei segmentation mask generation model constructed previously, and that masks a position of the nucleus of the living cell to derive an identification result. The system may further comprise a post-processing unit that derives a final result by applying a preset post-processing method to the derived identification result.

Methods and apparatus for segmenting cell nuclei in medical images apply first and second trained machine learning algorithms. The first trained machine learning algorithm processes a medical image to provide center locations of cell nuclei depicted in the image. The second machine learning algorithm processes each of a plurality of patches of the image. Each of the patches correspond to one of the plurality of center locations. Processing each patch yields a nuclear boundary corresponding to the corresponding one of the center locations. The methods and apparatus allow associating individual pixels of the image with one or more than one nuclei and have been shown to be effective for instance segmentation of nuclei in clusters of overlapping cell nuclei.

The present invention discloses a method, device and readable medium for spatial transcriptome analysis based on deep learning. By constructing and training an instance segmentation neural network, a cell nucleus segmentation model is obtained. The instance segmentation neural network includes a hierarchical transformation encoder and a fully connected decoder. The cell nucleus image is input into the cell nucleus segmentation model to obtain the cell nucleus segmentation result and determine the position information of the cell. The coordinates of ribonucleic acid molecules generated by spatial transcriptomics are obtained, and each ribonucleic acid molecule is assigned to the cell with the shortest distance from it according to the cell nucleus segmentation result and the coordinates of the ribonucleic acid molecules to form a single-cell expression matrix. According to the single-cell expression matrix, cell type annotation is performed on the cells to obtain the cell type, and the anatomical region is identified according to the cell type and the position information of the cell. The point cloud technology is used for three-dimensional visualization to reconstruct the surface contour of the tissue or organ. This method can perform cell segmentation quickly and accurately.

This application provides a method, apparatus, device, and storage medium for cell nucleus segmentation based on contour characteristics. The method includes: acquiring an image to be segmented; preprocessing the image to be segmented to obtain a preprocessed image; using a convolutional filter on the preprocessed image to determine a denoised image; determining effective cell nucleus contours based on the denoised image; and expanding the effective cell nucleus contours based on adjacent pixels to obtain segmented cell nuclei. This scheme can achieve high accuracy on real cervical cell images (i.e., the BSMMU dataset) while maintaining a fairly high recall rate.

This invention provides a method for cell nucleus segmentation in pathological images based on deep neural networks under weak supervision. The method includes: performing point annotation processing on sample images to generate a coarse supervision signal, including Venn diagram labels, cluster labels, and superpixel labels; using the Venn diagram boundary as a geometric prior, converting the superpixel labels into soft labels through an adaptive label smoothing strategy; constructing a segmentation network with an encoder-decoder structure, embedding a multi-domain edge module after each downsampling stage of the encoder to extract and enhance cell nucleus boundary features; embedding multi-faceted feature enhancement modules at the skip connections between the encoder and decoder to denoise, enhance, and structurally focus the features; and jointly training the network based on the supervision signal using a weighted multi-task loss function to obtain a trained segmentation model, which is then used to segment cell nucleus instances in the pathological image to be segmented. This invention achieves high-precision cell nucleus instance segmentation using only point annotations.

Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.

The present application relates to a kind of pathological image cell population feature extraction method and system, wherein the method comprises the following steps: obtaining pathological image;Pathological image is input into cell nucleus segmentation model, and cell nucleus segmentation result is obtained;Pathological image is cropped based on cell nucleus segmentation result, and the cell image of each cell in pathological image is obtained;Cell image is input into the cell feature extraction model based on contrast learning, and the feature and cell location information of each cell are obtained;The feature set of all cells and corresponding cell location information are input into the population feature extraction model based on contrast learning, and the cell population feature is obtained, wherein, population feature extraction model generates positive sample of contrast learning by randomly selecting rectangular region of different size, arbitrary rotation angle in pathological image during training process.Compared with prior art, the present application has the advantages of good robustness, strong generalization ability and strong explainability.

The invention provides a color cell image segmentation method and device and electronic equipment, and the method comprises the steps: carrying out the segmentation of a color cell original image through color deconvolution and a deep learning method, and obtaining a first cell nucleus segmentation image, the color cell original image being Hamp; an E cell image; classifying pixel points in the color cell original image by adopting a clustering algorithm to obtain a classification result corresponding to each pixel point; determining a second cell nucleus segmentation image according to the classification result; fusing the brightness values at the same pixel position in the first cell nucleus segmentation image and the second cell nucleus segmentation image to obtain a fused brightness value corresponding to a pixel at each pixel position; and generating a target cell nucleus segmentation image according to the fusion brightness value corresponding to the pixel at each pixel position. According to the method, cell nucleus segmentation is carried out through a method of combining color deconvolution, deep learning and a clustering algorithm, the false positive phenomenon in the segmentation result is improved, and the segmentation precision is improved.

The invention discloses a cell nucleus segmentation method based on weak supervision and semi-supervision, and the method comprises the following steps: S1, selecting any pathological image in a breast cancer pathological image library and part of point labels thereof as to-be-detected images, and carrying out the initial training of mark points through employing an extended Gaussian filtering algorithm; then, a background propagation method is adopted to carry out self-training, a relatively accurate cell nucleus detection result is obtained through continuous iterative updating, a course learning strategy is utilized, a model is trained by using a simple sample, and then the difficulty of the sample is gradually increased along with the improvement of the model so as to enhance the learning ability of the model; and S2, generating three weak tags by using the complete point tag obtained in the S1, wherein the three weak tags comprise: 1) generating a Voronoi tag; according to the method, the requirement on the model is reduced, limited weak label data can be utilized, relatively high segmentation accuracy can be kept, meanwhile, the separation efficiency is also improved, and processing of edge and shape information of the cell nucleus is facilitated.

The invention is applied to the technical field of medical image processing and artificial intelligence, and particularly discloses a histopathology image-oriented cell nucleus segmentation method and system, which comprises an encoder, and is characterized in that multi-scale features are extracted from an input pathological image through the encoder, and the multi-scale features are extracted from the input pathological image; the multi-scale feature is decoded by at least two parallel decoder branches, the parallel decoder branches including an NP decoder branch and an HV decoder branch, generating at least one nuclear pixel (NP) segmentation map and a horizontal / vertical (HV) distance map. According to the histopathologic image-oriented cell nucleus segmentation method and system, the powerful hierarchical feature extraction capability of Swin Transformer is combined with an instance perception multi-task framework of Hover-Net, and experiments on CoNSeP and MoNuSeg data sets prove that the Dice coefficient and the IoU index in the method are both superior to those of a baseline model Hover-Net and Swin-Unet, so that more accurate cell nucleus segmentation is realized.

In embodiments, a method includes reading a nuclear segmentation mask of an image including pixels arranged in two dimensions. The nuclear segmentation mask identifies cellular nuclei stained with a cellular nucleus stain. The method includes determining amplitudes for each pixel. Each amplitude corresponds to exactly one dimension of the two dimensions. The method includes constructing a graph having nodes and edges. Each node corresponds to a pixel. Nodes corresponding to neighboring pixels are connected by an edge. The method includes assigning a weight to each edge. Each edge's weight is based on amplitudes of pixels corresponding to nodes connected thereby. The method further includes, based on the graph, determining, for each cellular nuclei, a heat map corresponding to a predicted cell region associated with that cellular nucleus. The method further includes based on the heat maps of the cellular nuclei, determining a cellular segmentation mask comprising predicted cell regions.

The invention relates to a nuclear immunohistochemical image segmentation method based on SAM2 and HIPT models. For the data of breast cancer, bladder cancer and lung cancer slices, a preliminary segmentation mask is generated by using an SAM2 model, then the preliminary segmentation mask is input into an HIPT model, and a segmentation result is further optimized through multi-modal feature learning. In order to effectively fuse the tasks of the two stages, a joint loss function is designed, and global optimization is carried out by combining segmentation loss and multi-modal feature loss, so that the model not only pays attention to segmentation precision, but also can capture multi-modal information. According to the method, efficient cell nucleus segmentation is realized, the performance in the aspects of segmentation precision, robustness and the like is good, and the purpose of effectively carrying out cell nucleus segmentation on breast cancer, bladder cancer and lung cancer images is achieved.

The present application belongs to the technical field of medical image processing, and particularly relates to a gastrointestinal cancer pathological image segmentation method based on a ViT mechanism model and related equipment. The gastrointestinal cancer pathological image segmentation method based on the ViT mechanism model comprises the following steps: obtaining a gastrointestinal cancer pathological image to be segmented, and preprocessing the gastrointestinal cancer pathological image to be segmented; adopting a preset cell nucleus segmentation model based on a ViT main body to perform cell nucleus segmentation on the preprocessed gastrointestinal cancer pathological image, and generating two segmentation predictions; and adding the two segmentation predictions to obtain a prediction result, which is taken as a cell nucleus segmentation result. The present application is aimed at the problems of uneven staining of pathological images, irregular, overlapping gastrointestinal cancer cell nuclei, and insufficient segmentation accuracy of previous methods. The gastrointestinal cancer pathological image segmentation method based on the ViT mechanism model can automatically, efficiently and accurately detect and segment the cell nucleus region in the gastrointestinal cancer pathological image.

The present application relates to the technical field of data processing, and discloses a cell nucleus segmentation model construction method, a cell nucleus segmentation method and a construction device, the construction method comprising: obtaining a cell image dataset; constructing a cell nucleus segmentation network structure, the cell nucleus segmentation network structure comprising a patch embedding layer, an encoder module, a tokenized KAN network module, a decoder module and a projection layer connected in sequence, and the encoder module and the decoder module are further connected through a frequency domain learnable module; wherein the frequency domain learnable module is used for transmitting low-level features in the encoder module to the decoder module; the tokenized KAN network module is used for deep feature extraction; and the cell nucleus segmentation network is trained based on the cell image dataset to obtain a cell nucleus segmentation model. The cell nucleus segmentation model provided by the present application pays more attention to the cell nucleus boundary effect, greatly improving the cell nucleus segmentation precision.

A multi-organ nuclei segmentation method based on prompt learning is provided. The multi-organ nuclei segmentation method fully mines image information based on text and image information and learns an association between semantic information and a segmentation target, thereby achieving comprehensive learning for target region segmentation. The multi-organ nuclei segmentation method learns a large amount of text and image paired knowledge from six publicly available nucleus datasets based on a clip model to acquire prior knowledge for semantic understanding of nuclei, making a model fully suitable for nuclei segmentation. The constructed model inputs images and text prompts, and utilizes text and image information to achieve nucleus recognition and accurate nuclei segmentation of six different organs, improving computational efficiency. The model can also utilize sufficient text prompts to complete accurate segmentation tasks on some unlabeled datasets, achieving practicality and scalability.

The application discloses a double-task cycle self-supervision nucleus segmentation method based on dyeing similarity reconstruction, and comprises the following steps: establishing a prior knowledge guided self-supervision learning framework, generating reliable initial pseudo labels through self-supervision learning of dyeing prior knowledge and image prior knowledge; the prior knowledge guided self-supervision learning framework comprises a contrast learning model and a dyeing reconstruction model to help the framework learn image and dyeing prior knowledge; training a downstream double-task nucleus segmentation model, using inter-task consistency and intra-task self-refinement strategies to improve the performance of contrast learning and dyeing reconstruction, and realizing nucleus segmentation. The application realizes the initialization of pseudo labels through self-supervision learning and dyeing reconstruction of the prior knowledge of histopathology images; the double-task model is trained to realize nucleus segmentation, the inter-task consistency and intra-task self-refinement strategies are used to make up for the information gap, and the iterative refinement of the two tasks is maintained.

The application relates to an image data augmentation method for cell nucleus segmentation, which comprises the following steps: obtaining a randomly generated noise image, inputting the noise image into a preset denoising model for denoising processing to obtain a target image pair, wherein the target image pair comprises a target image and a corresponding labeled target image, and the preset denoising model is obtained by training based on sample cell images and sample segmentation images. In the above method, since the preset denoising model is obtained by training based on the sample image pair composed of the sample cell images and the sample segmentation images, the target image pair generated by the preset denoising model has certain difference from the sample image pair, and the diversity of the sample data set can be improved. In addition, by generating a random noise image and using the preset denoising model to perform denoising processing on the noise image, more sample pairs can be easily and accurately generated, and the accuracy of the image data augmentation is improved.

The invention belongs to the technical field of data processing, and particularly relates to a multi-modal cell nucleus segmentation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring original image data generated by a single molecule tracing experiment and single molecule positioning point data corresponding to the original image data; based on the original image data and the single molecule positioning point data, constructing input features including at least two different modal information; and inputting the input features into a pre-trained neural network model to generate a segmentation result of the cell nucleus. According to the method, multi-modal input features are constructed by relying on original image data and single-molecule positioning point data, and accurate and stable cell nucleus segmentation results can be output in weak-texture and low-signal single-molecule tracing experiments in combination with the neural network model.

The invention provides a system and a method for automatically segmenting deep grey matter nuclei in magnetic resonance imaging, and a medium. The method comprises the following steps: carrying out resampling, cutting and normalization preprocessing on a magnetic resonance image; using an encoder module comprising an alternating convolutional neural network encoder block and a Transform encoder block to extract hierarchical feature representation; through a cascade channel-space fusion module, features are fused by applying a channel and a space attention mechanism; reconstructing the segmentation map using a decoder module comprising an up-sampling layer and a hopping connection; through a symmetric boundary attention module, original and mirror image segmentation images are compared, and boundary refinement is enhanced; using a dynamic adaptive weighted Dice loss function to adjust the weight according to the volume of each nucleus, and guiding network training; and outputting a segmentation map of the deep grey matter nuclei. According to the method, accurate segmentation of the DGM nuclei is realized, and the problem of class imbalance in small-size nucleus segmentation is effectively solved.

The application discloses a kind of based on ultra-light medical image segmentation network's cell nucleus image segmentation method, constructs ultra-light medical image segmentation network, and ultra-light medical image segmentation network adopts the coding-decoding structure of U type, including ultra-light encoder module and ultra-light decoder module;Cell image is normalized;The cell image after pretreatment is input into ultra-light medical image segmentation network, and the deep semantic feature of cell image is extracted by ultra-light encoder module, obtains high-level feature map and inputs high-level feature map into ultra-light decoder module, recovers resolution layer by layer, finally outputs the cell nucleus segmentation result graph that resolution and channel number are same with input;The present application can accurately determine the outline position of cell nucleus, realize efficient cell nucleus segmentation, and has wide application prospect.

The application discloses a weakly supervised cell nucleus segmentation method based on wavelet difference convolution and region expansion, and relates to the technical field of image processing, and comprises the following steps: obtaining a cell nucleus image sample, and marking a cell nucleus position by using a center point annotation; designing a wavelet difference convolution module, extracting multi-scale features through discrete wavelet transform, and combining the difference convolution module to enhance cell nucleus boundaries and detail information; constructing a region expansion module, generating a pseudo label based on point annotation iteration, gradually expanding a complete cell nucleus region, and reducing noise and nucleus missing detection problems; building a segmentation network, adding the wavelet difference convolution module to extract detail features, and using the pseudo label as a weakly supervised signal to optimize network performance; performing segmentation prediction, and outputting accurate positions and shapes of cell nuclei; thereby realizing high-precision segmentation under a small amount of annotation information, reducing annotation dependence, and achieving remarkable effects in reducing adhesion of adjacent cell nucleus boundaries and missing detection of small cell nuclei.

The invention relates to the technical field of image processing, and discloses a DNA ploidy image depth denoising and screening method and system, and the method comprises the steps: eliminating the format difference of a DNA ploidy image, and obtaining a standardized image of the DNA ploidy image; determining a noise region and a noise type in the standardized image according to distribution characteristics of pixel gray values in the standardized image and neighborhood pixel relevance; selecting a gray value correction strategy corresponding to the noise region based on the noise type, and obtaining a denoised image of the standardized image by executing the gray value correction strategy; performing region segmentation on the de-noised image to obtain a cell nucleus segmentation image of de-noised image data; extracting DNA content characteristics in the cell nucleus segmentation image according to the pixel intensity value in the cell nucleus segmentation image; screening a ploidy abnormal region in the cell nucleus segmentation image according to DNA content characteristics, and generating a ploidy screening result of the DNA ploidy image; according to the method, the deep denoising and screening efficiency of the DNA ploidy image can be improved.