Cancer cell recognition method, device and system

Abstract

The invention discloses a cancer cell recognition method, device and system. The method comprises the steps of processing a cell image to obtain a first feature map; processing the first feature map to obtain a set; processing the set to obtain a second feature map, and processing the second feature map to obtain a cancer cell detection frame and a cancer cell code; processing the cancer cell detection frame is processed based on a deep learning network to output category information. According to the method, the cell image is processed to obtain the collection of the elliptical cell candidate frames, and the elliptical cell candidate frames can reduce background noise generated during subsequent processing compared with a normal rectangular frame; processing the set is processed to obtain a cancer cell detection frame and cancer cell codes, the segmenting cancer cells are segmented for type analysis, and reducing the subsequent image processing difficulty is reduced through the cancer cell codes; a cancer cell detection frame is processed to output type information, the type of cancer cells can be recognized through a mature deep learning network, and compared with an existing recognition mode, the cancer cell recognition method can better adapt to cancer cell recognition in a complex cell environment.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a cancer cell identification method, device and system. Background technique [0002] Cancer cells are mutated cells and the source of cancer. Cancer cells are different from normal cells in that they have three characteristics: infinite proliferation, transformation and easy transfer. They can infinitely proliferate and destroy normal cell tissues. In addition to the uncontrolled division of cancer cells (capable of multipolar division), cancer cells can also partially invade the surrounding normal tissues and even transfer to other parts of the body through the circulatory system or lymphatic system in the body, resulting in malignant tumors that are difficult to cure in the human body; the cure of existing cancer One of the main reasons for the low rate is that the period of cancer discovery is too late. Therefore, if the signs of cancer can be found in advance, and then reas...

AI Technical Summary

Problems solved by technology

In addition to the uncontrolled division of cancer cells (capable of multipolar division), cancer cells can also partially invade the surrounding normal tissues and even transfer to other parts of the body through the circulatory system or lymphatic system in the body, resulting in malignant tumors that are difficult to cure in the human body; the cure of existing cancer One of the main reasons for the low rate is that the period of cancer discovery is too late. Therefore, if the signs of cancer can be found in advance, and then reasonable treatment methods can be adopted, the treatment rate of cancer can be significantly improved

[0003] The existing algorithms for cancer cell identification are mostly based on the difference between the shape of cancer cells and normal cells to determine whether they belong to cancer cells; in order to reduce the difficulty of detection, a small part of cell tissue will be extracted for separation, and then For detection, the detected cells have the problem of thin cell layers, but in fact, due to the metastasis characteristics of cancer cells and the complexity of the location, the cell detection on the spot will require extraction of larger volumes with a variety of cells. The structure of the cell organization, so there is a large number of cells and a large number of cell layers, which leads to the problem of low recognition rate

Images