13 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.

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 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 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 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.

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.