Liver tumor segmentation method based on improved U-net network

Abstract

The invention relates to a liver tumor segmentation method based on an improved U-net network, and the method comprises the steps: 1, obtaining an abdominal CT data set, and carrying out the preprocessing operation; 2, before the liver tumor is segmented, segmenting a liver region, building a neural network for liver segmentation based on a Keras deep learning framework, and selecting tensor flowat the rear end; 3, training the liver segmentation network based on the improved U-net; 4, constructing a liver tumor segmentation network based on the improved U-net based on a Keras deep learning framework, and training the network; and 5, segmenting a liver region from the abdominal liver CT image by adopting a liver segmentation network based on improved U-net, and segmenting tumors and normal liver tissues from the liver region. The method not only can eliminate a large amount of wrong segmentation, but also can reduce the complexity of a network model.

Description

technical field [0001] The invention relates to a liver tumor segmentation method based on an improved U-net network, and belongs to the field of liver tumor segmentation methods. Background technique [0002] The percutaneous radiofrequency ablation (Radio Frequency Ablation, RFA) technology developed in recent years is currently the most widely used local minimally invasive treatment of liver tumors. Percutaneous radiofrequency ablation technology requires precise resection of liver tumor lesions, ensuring the integrity of non-lesional areas of the liver, reducing the amount of bleeding and trauma suffered by patients during surgery, and giving patients the best surgical treatment effect and Postoperative recovery effect. [0003] With the development of modern medical imaging technology, medical imaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) have appeared successively. Medical image segmentation technology ha...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to propose a liver tumor segmentation method based on the improved U-net network, to solve the traditional medical image segmentation method and the medical image segmentation method based on machine learning have relatively large limitations when completing the segmentation task of liver tumors sexual problems

Images