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A method of calculating cerebral hemorrhage volume based on depth learning

A technology of deep learning and multi-scale features, applied in computing, image data processing, image enhancement, etc., can solve the problems of high time cost and inapplicability to clinical applications, and achieve the effect of improving segmentation accuracy and fast segmentation speed

Pending Publication Date: 2018-12-14
INFERVISION MEDICAL TECH CO LTD
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Problems solved by technology

However, the time cost of manually segmenting the hemorrhage area by radiologists is too large, and this method is not suitable for clinical application

Method used

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  • A method of calculating cerebral hemorrhage volume based on depth learning
  • A method of calculating cerebral hemorrhage volume based on depth learning
  • A method of calculating cerebral hemorrhage volume based on depth learning

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Embodiment Construction

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will further describe in detail the embodiments of the present invention in conjunction with the accompanying drawings.

[0030] The general idea of ​​the present invention is to use the self-constructed network structure based on DenseNet to train the input CT image data of cerebral hemorrhage, so that the network can extract the best feature vector, and use this feature vector to test the cerebral hemorrhage. Classification of hemorrhage CT image data.

[0031] figure 1 It is a flow chart of the method for calculating cerebral hemorrhage volume based on deep learning according to the present invention. The method for calculating the amount of cerebral hemorrhage based on deep learning of the present invention comprises the following steps:

[0032] S1: Obtain and label CT image data of cerebral hemorrhage.

[0033] The present invention collected 3000 cas...

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Abstract

The invention provides a method for calculating intracerebral hemorrhage amount based on depth learning, which comprises the following steps: S1, obtaining CT image data of intracerebral hemorrhage and marking; S2, designing a segmentation model of an intracerebral hemorrhage region based on depth learning, and training the segmentation model by using the marked intracerebral hemorrhage CT image data; S3, inputting the tested CT image data of cerebral hemorrhage into the trained segmentation model to obtain the segmentation result; S4, calculating the volume of cerebral hemorrhage according tothe obtained segmentation result; S5: generating a structured report containing the location and volume of intracerebral hemorrhage. The invention can accurately and quickly calculate the amount of cerebral hemorrhage, and provides important decision information for clinical intervention.

Description

technical field [0001] The invention relates to the fields of medical imaging and artificial intelligence, and is a method for calculating cerebral hemorrhage based on deep learning. Background technique [0002] The incidence rate of hemorrhagic stroke is second only to ischemic stroke among all stroke subtypes, ranking second. The incidence of cerebral hemorrhage in the population is (12-15) / 100,000 person-years. Cerebral hemorrhage has the characteristics of dangerous onset, rapid change of condition, and high mortality and disability rate. More than 70% of patients have early hematoma expansion or involve the ventricle, and the mortality rate within 3 months is 20%-30%. Cerebral hemorrhage has also caused a heavy social and economic burden. In 2003, my country's statistics showed that the direct medical expenses of cerebral hemorrhage were 13.72 billion yuan per year. Early, active and reasonable treatment can improve the clinical outcome of patients. Bleeding volume ...

Claims

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Application Information

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IPC IPC(8): G06T7/62G06N3/04G06T7/11
CPCG06T7/11G06T7/62G06T2207/30016G06T2207/10081G06N3/045
Inventor 张荣国龚强夏晨陈宽
Owner INFERVISION MEDICAL TECH CO LTD
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