Liver tumor segmentation method and device based on CT (Computed Tomography) image

Abstract

The invention provides a liver tumor segmentation method and device based on a CT (Computed Tomography) image. The method comprises the following steps: performing Gaussian denoising on CT image data of a liver, converting the denoised CT image data into standardized data of which a gray average is 0 and a variance is 1, and performing down-sampling operation; extracting a lesion slice and a normal tissue slice from a gold standard image of the CT image of the liver, and classifying the lesion slice and the normal tissue slice into a positive sample and a negative sample; constructing a multi-level depth convolutional neural network, training a model through a stochastic gradient descent to obtain a network model, and acquiring a coarse segmentation binary image of a tumor and a pixel-classification probability image through a classifier; performing morphological erosion operation on the coarse segmentation binary image of the tumor to obtain a foreground image needed by graph cut, performing subtraction operation on the binary image of a liver and the coarse segmentation binary image of the tumor, and performing the morphological erosion operation to obtain a background image corresponding to normal tissues of the liver; and constructing an undirected graph, and obtaining a finial segmentation region of the tumor through a graph cut optimization algorithm.

Description

technical field [0001] The invention belongs to the field of medical image processing, in particular to a CT image-based liver tumor segmentation method and device. Background technique [0002] The liver is an important and complex functional organ to maintain the life activities of the human body. There are many types of liver lesions, and the incidence rate is high. Computed Tomography (CT) images have become one of the important routine means in clinical diagnosis and an important means of examination for liver diseases. Currently, the treatments for liver tumors mainly include tumor resection, interventional therapy, and radiotherapy, among which tumor resection is the most effective treatment. These treatment methods all require accurate knowledge of the number, location, size, and shape of the tumor before surgery, which is helpful for the formulation of a treatment plan for liver tumors. , size, shape, gray scale and texture are different, it is difficult to develo...

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Problems solved by technology

[0004] The technical problem to be solved in the present invention is to overcome th

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