A tooth CT image segmentation method based on deep learning

Abstract

The invention belongs to the technical field of medical CT (computed tomogram) image segmentation, and relates to a tooth CT image segmentation method based on deep learning. According to the technical scheme provided by the invention, a traditional Level Set algorithm and a U-net network model are combined, and a Level Set algorithm is used for solving the problem of a training set required by the neural network, so that the neural network can be trained by using unmarked tags, at the same time, the neural network model is used to complete the automatic segmentation of the image, the problemof non-convergence of curve evolution is avoided, and a sufficient and accurate segmentation effect can be obtained under the condition that a medical image training set is insufficient.

Description

technical field [0001] The invention belongs to the technical field of medical CT (Computed Tomography, computerized tomography) image segmentation, and relates to a tooth CT image segmentation method based on deep learning. Background technique [0002] The problem of medical CT image segmentation has a long history. Many researchers have proposed their own algorithms in this field to realize the segmentation of medical images, and achieved certain results. Due to the many limitations of medical images, there are still many problems in methods such as statistics-based segmentation, graph segmentation, active contour segmentation, and deep learning segmentation. The comparative analysis is as follows. [0003] Statistics-based segmentation methods include Otsu threshold method, multi-threshold control watershed method, adaptive threshold method, iterative threshold technique, etc. The calculation structure of this method is simple and easy to understand, and it is suitable ...

AI Technical Summary

Problems solved by technology

[0010] With the rapid development of computer technology and the update and iteration of GPU, today's computing speed is getting faster and faster, and the application of deep learning is becoming more and more extensive. However, the number of training set pictures used in deep learning training models is huge, and it is difficult for medical images to provide sufficient and effective data. CT images, and artificially marked images also have serious time-consuming problems, which can easily cause data overfitting problems and make the model unable to be applied to images outside the training set. This is also the main reason why deep learning in the field of medical images is far behind natural image processing. reason

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