Medical image segmentation method based on lightweight full convolutional neural network

Abstract

The invention provides a medical image segmentation method based on a lightweight full convolutional neural network. The method comprises the following steps: carrying out preprocessing such as graying, normalization, contrast limited adaptive histogram equalization (CLAHE) and gamma correction on a data set; randomly extracting patches from the training set and sequentially extracting patch graphs from the test set to complete data enhancement; building a full convolutional neural network architecture composed of a contraction path (left side) and an expansion path (right side), and designinga left-one-out training method for a data set with a small number of images; and finally, completing BN channel model cutting through channel sparse regularization training, cutting channels of whichscaling factors are smaller than a set threshold, finely adjusting the cut network to obtain a lightweight full convolutional neural network, and inputting test data into the network for rapid test to complete image segmentation. The lightweight full convolutional neural network not only ensures the advantage of high segmentation precision of the deep network, but also improves the test speed ofthe image segmentation network.

Description

technical field [0001] The invention belongs to the technical field of image feature extraction and segmentation methods, in particular to a medical image semantic segmentation method for a lightweight fully convolutional neural network. Background technique [0002] Fully Convolutional Neural Network (FCN) has dominated the field of image segmentation, especially semantic segmentation, because it has the characteristics of accepting input images of any size, high computing speed, etc., and can achieve pixel-level image segmentation effects. [0003] At present, a lot of experiments have been done on the fully convolutional neural network. Since FCN in 2015, a batch of classic models have been produced, such as UNet and UNet-like structures. These models are based on FCN and continuously deepen the number of network layers to improve the network. of the segmentation accuracy. But it also brings a series of problems, such as high network training environment requirements, sl...

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Problems solved by technology

It is widely used in convolutional neural networks, but the research and applicatio

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