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Fully-automatic three-dimensional liver segmentation method based on convolution nerve network

A convolutional neural network, fully automatic technology, used in image analysis, image data processing, instruments, etc., can solve the problems of long segmentation time, low contrast and weak boundaries, over-segmentation, etc., to avoid under-segmentation and over-segmentation, accurate The effect of liver segmentation results

Active Publication Date: 2015-10-21
ZHEJIANG DE IMAGE SOLUTIONS CO LTD
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Problems solved by technology

Due to the low contrast and weak boundary of CT images, the current automatic methods generally have over-segmentation and under-segmentation, so some methods will perform some complicated preprocessing before segmenting the liver, such as the pre-extraction of surrounding organs, But this will cause the problem of too long segmentation time

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  • Fully-automatic three-dimensional liver segmentation method based on convolution nerve network
  • Fully-automatic three-dimensional liver segmentation method based on convolution nerve network
  • Fully-automatic three-dimensional liver segmentation method based on convolution nerve network

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Embodiment Construction

[0030] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0031] Provide a new fully automatic 3D liver segmentation method based on convolutional neural network, which is used to segment abdominal liver CTA (Computed Tomography Angiography, CT angiography) volume data, that is, the liver in computerized tomography angiography images, Include the following procedures:

[0032] 1. Prepare the training set;

[0033] 2. Training convolutional neural network;

[0034] 3. Use the trained convolutional neural network to process the abdominal liver CTA volume data to obtain the liver segmentation results.

[0035] Described process one specifically comprises the following steps:

[0036] Step A: Collect 68 abdominal liver CTA volume data with a size of 512×512×N, and provide the liver segmentation standard results of these data by doctors and experts, where N is the number of layers of volume data.

[003...

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Abstract

The invention relates to the field of medical image processing and aims to provide a fully-automatic three-dimensional liver segmentation method based on a convolution nerve network. The fully-automatic three-dimensional liver segmentation method based on the convolution nerve network comprises the following processes: preparing a training set, and training the convolution nerve network, processing CTA volume data of an abdominal liver by utilizing the trained convolution nerve network to obtain a segmentation result of the liver. The liver is segmented by means of the three-dimensional convolution nerve network; the three-dimensional liver segmentation method is fully automatic and can also prevent under-segmentation and over-segmentation phenomena well; and the accurate liver segmentation result is obtained.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a fully automatic three-dimensional liver segmentation method based on a convolutional neural network. Background technique [0002] At present, liver disease is a disease with a relatively high incidence rate clinically, which directly threatens people's lives, so the accurate diagnosis of liver disease has important medical significance. Clinically, doctors often use a CT machine, that is, a computerized tomography machine, to obtain a series of planar grayscale tomographic images of the liver, and to judge the lesion location, characteristics, size, and surrounding tissue of the lesion by continuously viewing these images. relationship between etc. The extraction and quantitative analysis of the liver play a key role in accurately diagnosing liver diseases and formulating appropriate surgical plans. Clinically, the extraction of the liver is often outlined directly on...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/10088G06T2207/20084G06T2207/30056
Inventor 孔德兴吴法卢方
Owner ZHEJIANG DE IMAGE SOLUTIONS CO LTD
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