Full-automatic segmentation method for kidney tumors

A kidney tumor, fully automatic technology, applied in image analysis, image data processing, image enhancement and other directions, can solve the problems of gradient dispersion, low algorithm boundary segmentation accuracy, etc.

Pending Publication Date: 2021-06-25
CHENGDU UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Due to the great advantages of the UNet algorithm for medical image segmentation, the present invention provides a RAUNet automatic kidney segmentation system based on the UNet algorithm,

Method used

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  • Full-automatic segmentation method for kidney tumors
  • Full-automatic segmentation method for kidney tumors
  • Full-automatic segmentation method for kidney tumors

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Embodiment Construction

[0019] Depend on figure 1 It can be seen that the experimental platform of this application is based on the Windows 10 operating system, including:

[0020] CPU: The memory of the CPU is 16G. As the computing and control core of the computer system, it is the final execution unit for information processing and program operation.

[0021] GPU: NVIDIA GeForce GTX 1080Ti is an NVIDIA series graphics card, using 16nm process GP102 core, with 3584 CUDA cores, 224 texture units, 88 ROP units, with 352-bit11GB large-capacity video memory, the equivalent frequency is increased to 11GHz.

[0022] Programming language: Python provides efficient high-level data structures and simple and effective object-oriented programming. Python's syntax and dynamic typing, as well as its interpreted nature, make it a programming language for scripting and rapid application development on most platforms.

[0023] CUDA: CUDA (Compute Unified Device Architecture) is a computing platform launched by g...

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Abstract

The invention discloses a full-automatic segmentation method for kidney tumors. The full-automatic segmentation method comprises an experimental environment part and an algorithm research part. The experimental platform of the invention is a Windows 10 operating system, and the display card of the experimental platform is NVIDIA GeForce GTX 1080Ti. The algorithm research mainly comprises coarse segmentation of a CT image kidney and fine segmentation of a kidney tumor. The operation process of the whole system is as follows: 1, installing a Windows 10 operating system, and configuring an Anaconda version to be Anaconda 3, a python version to be 3.6, a CUDA version to be 10.0, a PyTorch version to be 1.6 and other required installation packages; 2, carrying out preprocessing operation on the data set, wherein the operation is operated in a pycharm which is provided with an Anaconda environment; 3, segmenting the processed data set by using the RAUNet. 4, carrying out post-processing on the pictures output after training. and 5, evaluating the model on the test set through an evaluation index. The main purpose of the invention is to promote the development of intelligent medical treatment, save labor cost, improve segmentation precision, and prevent diseases in advance, thereby ensuring human health.

Description

technical field [0001] The invention belongs to the field of the combination of medical imaging and artificial intelligence, and relates to a fully automatic segmentation technology for segmenting kidney tumors. Background technique [0002] The kidney is one of the essential organs of the human body. Its main function is to remove metabolites, wastes, and toxins from the body, and at the same time retain water and other useful substances needed by the body through reabsorption. With the increase of unhealthy diet and environmental pollution, kidney cancer has gradually become one of the most common cancers in the world, and it has caused serious harm to human health. According to statistics, renal tumors are one of the ten most common malignant tumors in humans, accounting for about 3%-5% of the tumor incidence. Furthermore, the manifestations and symptoms of renal tumor recurrence are not obvious enough to be noticed by people. Once discovered, it will be at an advanced s...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/155G06N3/04G06N3/08G16H30/20
CPCG06T7/11G06T7/155G06N3/04G06N3/08G16H30/20G06T2207/10081G06T2207/20036G06T2207/30084G06T2207/30096
Inventor 曾维郭敬娜于森
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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