A cervical cell pathological slice pathological cell segmentation method and system

Abstract

The invention discloses a cervical cell pathological slice pathological cell segmentation method and system, in particular, an off-line training sample set of cervical cell pathological slice pathological cell is established, a multi-scale cavity convolution structure semantic segmentation network is introduced on the basis of a depth residual network, and the semantic segmentation model is trained. The unit to be identified is extracted from the cervical pathological slice image, and different types of pathological cells are segmented in the unit to be identified by using the trained semanticsegmentation model. Based on the morphological features of pathological cells, a contour deformation model is established to further optimize the semantic segmentation results. According to the cellnumber and confidence level of different pathological types, the pathological types of the whole slice are predicted. The invention utilizes semantic segmentation network model and deformation variational model to accurately segment different types of pathological cells on the cell pathological slice image, and simultaneously improves the recognition accuracy and the recognition efficiency.

Description

technical field [0001] The invention belongs to the field of medical cytopathological image processing, and more specifically relates to a method and system for segmenting lesion cells in cervical cytopathological slices. Background technique [0002] Cervical cancer is a malignant tumor with high incidence in women. Cervical liquid-based cytopathology is currently the most important means of preventing and screening cervical cancer. Accurate interpretation of diseased cells in cytopathological slice images is an important basis for doctors to determine the patient's condition and formulate a treatment plan. At present, the manual interpretation of cytopathological images is not only time-consuming, but also the interpretation results are very dependent on the experience of doctors. Therefore, automatically interpreting diseased cells in pathological slides can not only improve the efficiency of diagnosis but also provide doctors with a more unified and objective basis for...

AI Technical Summary

Problems solved by technology

Artificially designed pathological cell feature engineering is effective but has great limitations

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