Cervical cancer cell segmentation method and system

Abstract

The invention relates to a method for segmenting cervical cancer cells. The method includes the following steps that noise of a cervical image is eliminated; a cytoplasm template is constructed for the image of which the noise is eliminated to perform rough segmentation, so that a cytoplasm area is obtained through segmentation; super-pixels are calculated for the cytoplasm area obtained through segmentation; the cytoplasm area which the super-pixels are calculated for is classified by the adoption of a convolution neural network; according to the constructed cytoplasm template of the image of which the noise is eliminated, cell nucleuses are roughly segmented; the roughly segmented cell nucleuses are corrected, and therefore segmentation on the cervical cancer cells is finished. The invention further relates to a system for segmenting the cervical cancer cells. On one hand, processing speed is guaranteed, and on the other hand, an accurate segmentation effect is achieved.

Description

technical field [0001] The invention relates to a method and system for segmenting cervical cancer cells. Background technique [0002] Cervical cancer is the 4th leading cause of death among malignant tumors in women, and 85% of women suffering from cervical cancer come from developing countries. Data have demonstrated that cervical cancer screening successfully reduces morbidity and mortality. Clinical screening methods are mainly cytology, colposcopy and histopathology, among which cytology screening is the simplest and most important means. [0003] Computer-aided cell detection technology is currently considered to play an important role in cytological screening. It can not only accurately screen, reduce labor intensity and workload, but also eliminate psychological adaptability and fatigue caused by manual detection. misdiagnosis and missed diagnosis. The performance of the computer-aided system mainly depends on the accurate image segmentation, so the accuracy of t...

AI Technical Summary

Problems solved by technology

[0005] Although these methods have achieved certain success, there is a very large distance between these methods and the actual auxiliary system: First, these methods only complete the segmentation of the nucleus, while ignoring the segmentation of the cytoplasm, and the information of the cytoplasm is important for identifying abnormal cells. It is also very critical; the second is that many of these methods assume that the input image only contains a single cell, so it is considered that there is only the boundary of the nucleus or the boundary of the nucleus and the cytoplasm in the image, but in reality, the irregular overlapping of multiple cells, Intersection, arrangement, the involvement of white blood cells, the influence of dust and impurities, and uneven illumination all increase the difficulty of actual segmentation

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