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Typing method, device and equipment

A classification method and neural network model technology, applied in the field of classification methods, devices and equipment, can solve problems such as limiting the effectiveness of endovascular treatment, difficulty in imaging diagnosis, and increasing complications.

Pending Publication Date: 2021-01-05
UNION STRONG (BEIJING) TECH CO LTD
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Problems solved by technology

[0004] The embodiment of this specification provides a classification method, device and equipment to solve the following technical problems: after middle cerebral artery occlusion, imaging diagnosis is difficult due to compensation delay, thereby limiting the effectiveness of endovascular treatment, and increasing Complications occurred

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  • Typing method, device and equipment
  • Typing method, device and equipment

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Embodiment 1

[0119] CTA or MRA image data, after normalization and resampling, is a matrix of 512*512*256, that is, the image is a tomogram of 256 frames, and each tomogram is a grayscale image of 512*512, so the number of channels is 1. In the framework of the open source machine learning platform Tensorflow or in the framework of the open source machine learning platform Pytorch, the input of the network is a matrix of 512*512*256*1. The first convolution module (inside the dotted line box) consists of two convolution blocks, each convolution block has a convolution operation and a pooling operation, and generally uses a 3*3*3 convolution kernel, which can be done after the convolution operation dropout (random inactivation) operation, normalization (normalization) operation, activation operation, etc., the pooling operation can be max pool (maximum pool) or average pool (average pool), etc., in the current embodiment, the pooling operation The step size is 2. The first convolution bloc...

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Abstract

The embodiment of the invention discloses a typing method, device and equipment, and belongs to the technical field of medical images and computers. The method comprises the following steps: acquiringto-be-processed image data; preprocessing the to-be-processed image data to obtain preprocessed image data; and based on a convolution module, a global feature fusion module and a classification module, carrying out typing on the preprocessed image data to obtain a typing result of the to-be-processed image data, the convolution module comprising a first convolution module and a second convolution module. By adopting the method provided by the embodiment of the invention, the non-acute occlusion imaging diagnosis of the middle cerebral artery can be quickly, comprehensively and accurately carried out, the typing of the cerebral infarction or the scoring of the cerebral infarction, such as ASPECTS scoring, can be realized, and a reference basis is provided for clinical treatment.

Description

technical field [0001] This specification relates to the field of medical imaging and computer technology, in particular to a classification method, device and equipment. Background technique [0002] Non-acute occlusion of intracranial arteries is an important cause of ischemic stroke, accounting for about 10% of all ischemic strokes, and the annual recurrence risk of stroke is 3.6% to 22.0%; middle cerebral artery occlusion is more common clinically , accounting for 79.6% of occlusive cerebrovascular diseases. At present, drug therapy is still the main treatment for symptomatic non-acute occlusion of intracranial arteries for more than 24 hours. Patients who fail drug therapy can also undergo intracranial and intracranial bypass surgery and endovascular therapy to reconstruct blood supply. [0003] Studies have shown that it is feasible and safe to receive endovascular treatment for non-acute occlusion of the middle cerebral artery. development of this technology. The m...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H30/20G16H10/20G16H50/30G06K9/62G06N3/04G06N3/08G06T7/00G06T7/11G06T7/136
CPCG16H50/20G16H30/20G16H10/20G16H50/30G06T7/0012G06T7/11G06T7/136G06N3/08G06T2207/10088G06T2207/30016G06T2207/30104G06N3/045G06F18/2415
Inventor 金海岚卢旺盛印胤杨光明秦岚
Owner UNION STRONG (BEIJING) TECH CO LTD
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