CT image liver tumor segmentation method based on deep learning

Abstract

The invention relates to a CT image liver tumor segmentation method based on deep learning, and specifically solves the problems that the original U-Net depth is small, the structure is simple, a model with a good training effect is difficult to train, and the accuracy of CT image liver tumor segmentation is insufficient. Specifically, the network structure of the U-Net is optimized and improved;a Dropout layer and a Batch Normalization layer are added to modify a network structure, a VGG-16 network module is used to replace an encoder part of U-Net, and an optimized model is established to perform accurate segmentation on a liver tumor CT image.

Description

technical field [0001] The invention belongs to the field of medical image processing, and relates to a method for segmenting liver tumors in CT images based on deep learning. Background technique [0002] The liver is the largest solid organ in the abdominal cavity of the human body, and it has a very complex structure and abundant blood vessels. And the liver has many types of lesions and a high incidence rate. The precise segmentation of liver tumor images plays a vital role in the treatment of the liver, but due to the complexity of the size, shape, and location of liver tumors, its segmentation is difficult and computationally intensive for traditional machine learning methods . With the rapid development of deep learning in the field of machine vision and the rapid growth of the number of medical images, medical image analysis based on deep learning has gradually become an important auxiliary tool for treatment. [0003] Many researchers have done a lot of research ...

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Problems solved by technology

"Fully Convolutional Networks for Semantic Segmentation.", Long, Jonathan, Shelhamer, Evan and Darrell, Trevor, The IEEE Conference on ComputerVision and Pattern Recognition (CVPR

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