Tumor region segmentation method and system for liver CT image based on cascaded full convolutional network

Abstract

The invention discloses a tumor region segmentation method and system for a liver CT image based on a cascaded full convolutional network. According to the method, automatic segmentation of a liver tumor region is realized by using a full convolutional network model. The method comprises the following steps: performing preprocessing operations such as filtering and sharpening enhancement on a CT image; training a cascade network by using the preprocessed CT data set; achieving liver region segmentation by using two stages of FCN networks and segmenting a tumor region from a liver region of interest. A first-stage FCN network uses a variable pooling method so that more liver features are reserved and liver segmentation precision is improved; and a second-stage FCN uses hole convolution replaces an original convolution layer and a pooling layer, so that the position information of the target can be reserved while a image is reduced and the feeling domain is increased. A large number of features do not need to be manually extracted, and the tumor region can be effectively and accurately segmented from the liver CT image.

Description

technical field [0001] The invention relates to the field of medical image processing and computing, in particular to a method and system for segmenting liver CT tumor regions based on a cascaded full convolution network. Background technique [0002] Abdominal CT imaging is one of the commonly used diagnostic methods for liver tumors. Since the treatment of liver tumors requires accurate knowledge of the size, location, and number of tumors before surgery, tumor segmentation in liver CT images has important clinical significance. However, the contrast of liver CT images is low, the size, shape and position of tumors are not fixed, and the distribution of intra-abdominal tissues and organs is complex, and the boundaries between normal liver tissues and tumors are blurred. Research hotspots and difficulties. [0003] In recent years, with the development of artificial intelligence technology, deep learning has achieved remarkable results in the field of image analysis, and ...

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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the deficiencies of the prior art, the present invention provides a method for segmenting liver CT tumor regions based on a cascaded full convolutional network, which can solve the problem of low image segmentation accuracy in the prior art. It also provides a segmentation system for liver CT tumor regions based on cascaded full convolutional networks

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