Small sample tooth detection method based on prototype segmentation network and storage medium

Abstract

The invention relates to a small sample tooth detection method based on a prototype segmentation network and a storage medium, a network inputs an analog image and a real image, two sample input networks can respectively obtain virtual image features and real image features after CNN feature extraction, the analog virtual image features are combined with a Mask label, and the real image features and the real image features are combined. Then feature clustering is carried out through an NPR module, real image features are divided into two branches, one branch obtains a preliminary segmentation result under feature guidance of the clustering features, the other branch carries out target detection network, a finally detected result and the preliminary segmentation result are fused, and the segmentation and detection results are mutually promoted to realize target detection. And an accurate tooth segmentation result is obtained. According to the method, the problem of small sample tooth segmentation is effectively solved, the respective advantages of target detection and semantic segmentation are utilized, mutual promotion and mutual learning are realized, the problems that tooth segmentation types are wrong and tooth boundaries are inaccurate are solved, and a large amount of time cost and labor cost can be saved.

Description

technical field [0001] The invention relates to the technical field of stomatology imaging, in particular to a small-sample tooth detection method and a storage medium based on a prototype segmentation network. Background technique [0002] With the development and progress of society, people's living standards have been greatly improved, and lifestyles have become more and more diversified. This has also produced many bad living habits that cause oral problems. People pay more and more attention to the condition of oral cavity and teeth. According to oral images, the overall structure of the oral cavity can be observed, and then the case data can be recorded. However, in the existing tooth detection schemes, the problems of wrong tooth segmentation and inaccurate tooth boundaries are particularly prominent, and a large number of samples are often required for model training, which cannot be done in Tooth detection is carried out when the number of tooth data and labels is s...

AI Technical Summary

Problems solved by technology

[0006] The purpose of this application is to solve and overcome the deficiencies of existing technologies and applications, to provide a small-sample tooth detection method and storage medium based on the prototype segmentation network, which effectively solves the problem of small-sample tooth segmentation, while using target detection and semantic The respective advantages of segmentation, mutual promotion and mutual learning, solve the problems of wrong tooth segmentation types and inaccurate tooth boundaries, and can save a lot of time and labor costs

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