25 results about "Nuclei segmentation" patented technology

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.

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.

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.

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 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.

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.

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 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.

The present invention relates to the technical field of artificial intelligence. Provided are a method and system for interpreting ER and PR expression in breast cancer by using artificial intelligence. The method comprises: collecting annotated pathological section images of breast cancer tissue, and performing data enhancement to obtain a sample image set; performing color difference balancing on image data in the sample image set to obtain a preprocessed data set; using the preprocessed data set to train a preset initial segmentation network; inputting the pathological section images of breast cancer tissue into a trained cell nucleus segmentation model to obtain a cell nucleus region image; morphologically processing the cell nucleus region image to obtain a processed target region; and extracting color features and morphological features within the target region, and inputting same into a classification model to obtain an interpretation result. The present invention uses a deep learning model to perform cell nucleus segmentation and feature extraction, thereby reducing subjectivity and errors brought by manual interpretation and improving the accuracy of interpretation.

The invention discloses a breast cancer tumor ploidy state prediction method based on a pathological image and application of the breast cancer tumor ploidy state prediction method. The breast cancer tumor ploidy state prediction method comprises the steps of obtaining a dyed pathological section image of a prediction target; segmenting the dyed pathological section image based on an image segmentation model to obtain a region-of-interest image; segmenting and classifying cell nucleuses in the image of the region of interest based on a cell nucleus segmentation and classification model to obtain binary mask information; performing multi-dimensional feature extraction on the binary mask information to obtain morphological features, textural features and spatial topological features of each cell nucleus; aggregating the features to obtain a sample feature matrix; and inputting the sample feature matrix into a classifier to obtain the tumor ploidy state of the prediction target. According to the method, accurate prediction of the tumor ploidy state can be realized only by relying on conventional slices, the method can be embedded into an existing pathological workstation, and the method is suitable for resource-limited scenes and has important supplementary value in clinical typing and treatment strategy formulation of breast cancer.

A multi-organ cell nucleus segmentation method based on cue-based learning, belonging to the field of medical image processing technology, fully mines image information by utilizing text and image multimodal information, learns the correlation between semantic information and segmentation targets, and comprehensively learns the segmentation of target regions. Based on the clip model, it learns a large amount of text and image pairing knowledge from six publicly available cell nucleus datasets to obtain semantic understanding prior knowledge of cell nuclei, making the model fully suitable for cell nucleus segmentation tasks. The model is constructed by inputting images and text cues, utilizing text and image multimodal information to complete the identification and accurate segmentation of cell nuclei of six different organs, with higher computational efficiency. This model can also complete accurate segmentation tasks on datasets with insufficient annotations using sufficient text cues, making it more practical and scalable.

The invention discloses a pathological section cell nucleus segmentation method based on gray-level co-occurrence matrix texture learning and density map guidance, and the method comprises the steps: 1), obtaining pathological section data, carrying out the labeling processing of the pathological section data, and carrying out the collection of the data, and building a material set; step 2), constructing a neural network model, importing the material set into the neural network model for training and testing until the model converges, and obtaining a cell nucleus segmentation network model; step 3), processing to-be-processed pathological section data containing the pathological image by using the cell nucleus segmentation network model to obtain a segmentation result; the present invention provides a method for providing a composition from Hamp; e, a neural network model for automatically segmenting a cell nucleus in a pathological section is combined with the Dedikley distribution uncertainty weighted learning gray-level co-occurrence matrix statistical texture information to enhance the capturing ability of the model for a fine structure, and the method can improve the segmentation accuracy and ensure effective identification of nuclear space distribution characteristics at the same time. The method is suitable for accurate kernel segmentation tasks in pathological images.

The invention discloses a medical event prediction method and device, equipment and a storage medium. According to the specific technical scheme, the method comprises the steps of obtaining a plurality of image slices of a pathological image; calculating a first prediction probability of the pathological image by using the plurality of image slices of the pathological image and a multi-instance learning algorithm; determining a cell image of the tumor cell in each image slice by using a cell nucleus segmentation classification model; acquiring pathological features of the tumor cells based on the cell images of the tumor cells; calculating to obtain a second prediction probability by utilizing pathological characteristics of the tumor cells and a multi-instance learning algorithm; and performing weighted summation on the first prediction probability and the second prediction probability to obtain a tumor recurrence medical event prediction result. And the prediction results of the two prediction models are integrated to determine the final tumor recurrence risk prediction result, so that the accuracy of the prediction result is improved.

The application discloses a kind of center point-based nuclear segmentation method and related equipment, the method includes: based on the pseudo-label generation algorithm of unsupervised clustering, nuclear image clustering is generated the instance pixel-level pseudo-label required when nuclear segmentation network model training;After obtaining the pseudo-label instantiation result of three classifications, the training of nuclear segmentation network model is carried out using the instance segmentation framework not dependent on non-boundary box;Nuclear center point detection network is trained, and the predicted nuclear center point is obtained by taking local maximum value and filtering operation;The segmentation result and detection result of the nucleus are fused and cut processing, and the corrected nuclear instance segmentation result is obtained.The present application processes pseudo-label into the classification graph of kernel-contour-background three classifications, so that the network can better focus on the contour of the nucleus, predict the nucleus center point, use the watershed algorithm to process the segmentation result, and improve the effect of adherent nuclear instantiation.