5 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.

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.

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.