A primary tumor cell segmentation and recognition method and system based on depth learning

Abstract

The invention relates to a primary tumor cell segmentation and recognition method and system based on depth learning, which is characterized in that the method comprises a model construction module, amodel training module and a model prediction module. the model building module is used to build the depth full convolution neural network model based on the artificial neural network in depth learning; The model training module is used to train the deep total convolution neural network model according to the training set, and the trained deep total convolution neural network model is obtained. The model prediction module is used to input the original image segmentation into the trained depth full convolution neural network model, and the segmentation results of cancer cell regions of each segmented image are aggregated to obtain the segmentation results of cancer cell regions of the original image. The invention has high accuracy and can be widely applied in the field of biomedical imageprocessing.

Description

technical field [0001] The invention belongs to the field of biomedical image processing, and in particular relates to a primary tumor cell segmentation and identification method and system based on deep learning applicable to various types of cancer cells. Background technique [0002] With the advent of the era of personalized medicine, drug screening and evaluation for individual cancer patients has also faced new challenges. One method of anticancer drug screening is primary culture, that is, to culture cancer cells in 96-well plates by using puncture tissues and effusions of individual patients, and add multiple groups of drugs, and observe the survival in each well after a period of time. How many cancer cells. However, the culture environment formed in this way contains cancer cells, various normal cells and impurities, so all kinds of cells in the photos taken by CCD exist. In this case, it takes a lot of manpower and time to determine which forms are cancer cells,...

AI Technical Summary

Problems solved by technology

However, the culture environment formed in this way contains cancer cells, various normal cells and impurities, so there are all kinds of cells in the photos taken by CCD

In this case, it takes a lot of manpower and time to judge which forms are cancer cells, and usually only semi-quantitative or even qualitative judgments can not accurately evaluate the effect of each group of anticancer drugs on cancer cells

However, traditional image recognition algorithms are difficult to achieve good results in the face of such complex photos.

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