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Pancreatic neuroendocrine tumor automatic segmentation method and system based on deep learning

A neuroendocrine and automatic segmentation technology, applied in the field of medical image processing, to achieve the effect of small experience influence, time and energy saving, and high efficiency

Active Publication Date: 2019-07-23
SHENZHEN UNIV
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Problems solved by technology

[0006] At present, there is no report on the application of deep learning to the CT-enhanced images of pancreatic neuroendocrine tumors for automatic lesion segmentation

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  • Pancreatic neuroendocrine tumor automatic segmentation method and system based on deep learning

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Embodiment Construction

[0059] First the nouns or terms involved in the present invention are explained:

[0060] CT: Computed Tomography, computerized tomography;

[0061] Gold standard (Ground truth, GT): Refers to the most reliable, accurate and best diagnostic method recognized by the current clinical medical community for diagnosing diseases. The commonly used clinical gold standards include histopathological examination (biopsy, autopsy), surgical findings, imaging diagnosis (CT, MRI, color B-ultrasound), pathogen isolation and culture, and conclusions from long-term follow-up;

[0062] HU: Heat Unit, which is the heat capacity unit of tubes in DR, CT and other medical equipment;

[0063] k-fold cross validation (k-fold crossValidation): in machine learning, the data set A is divided into training set (training set) B and test set (test set) C, in the case of insufficient sample size, in order to fully Use the data set to test the effect of the algorithm, divide the data set A into k parts ra...

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Abstract

The invention discloses a pancreatic neuroendocrine tumor automatic segmentation method and system based on deep learning. The method comprises the steps of obtaining a computed tomography enhanced image of a pancreatic neuroendocrine tumor patient; adopting a deep learning method to automatically segment the focus of the obtained computed tomography enhanced image, wherein the deep learning method adopts a deep convolutional neural network. According to the invention, automatic focus segmentation is carried out on the obtained computed tomography enhanced image by using a deep learning method; deep learning and computed tomography enhanced images are combined and applied to lesion segmentation of pancreatic neuroendocrine tumors. The method can automatically segment the focus area of thetumor through the feature learning of the deep convolutional neural network, is less affected by the experience of a doctor, is more accurate, saves the time and energy of the doctor for manually sketching the focus area, and is higher in efficiency. The method can be widely applied to the field of medical image processing.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to an automatic segmentation method and system for pancreatic neuroendocrine tumors based on deep learning. Background technique [0002] Pancreatic neuroendocrine neoplasms (pNENs), formerly known as islet cell tumors, have an incidence of about 1 / 100,000 to 4 / 100,000, accounting for about 3% of primary pancreatic tumors. Surgery is the main treatment for pNENs, and it is currently the only possible cure for pNENs. Localization diagnosis plays an important role in the surgical treatment of pNENs. Clinically, enhanced medical images are used to locate the tumor and show the tissue structure around the tumor, so as to outline the lesion area and guide doctors to perform surgical treatment. [0003] In the localization diagnosis of pNENs, the most commonly used imaging examination method is computed tomography (Computed Tomography, CT). In pNENs, like other vascular lesions, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11
CPCG06T7/11G06T2207/20084G06T2207/20081G06T2207/10081G06T2207/30096
Inventor 黄炳升林晓艺高樱榕肖焕辉罗宴吉冯仕庭宋晨宇陈洁
Owner SHENZHEN UNIV
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