Cerebral hematoma segmentation method and system based on deep learning

A technology of deep learning and cerebral hematoma, applied in the field of deep learning-based cerebral hematoma segmentation method and system, can solve problems such as waste of time, hardship, poor accuracy and repeatability, and achieve high efficiency, reduce information loss, and recognition accuracy high effect

Pending Publication Date: 2020-10-09
XUZHOU NORMAL UNIVERSITY
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Problems solved by technology

Manual segmentation is extremely time-consuming a

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  • Cerebral hematoma segmentation method and system based on deep learning
  • Cerebral hematoma segmentation method and system based on deep learning
  • Cerebral hematoma segmentation method and system based on deep learning

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Embodiment Construction

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0038] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0039] figure 1 The flow chart of the deep learning-based cerebral hematoma segmentation method provided by Embodiment 1 of the present invention, such as figure 1 As shown, the flow chart of the deep learning-ba...

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Abstract

The invention discloses a cerebral hematoma segmentation method and system based on deep learning. The method comprises the following steps: constructing a neural network model, the neural network model comprising a plurality of image information compression modules which are connected in sequence and a plurality of image information fusion modules which are connected in sequence, wherein the image information compression module comprises a first self-attention convolution unit, a second self-attention convolution unit and a pooling layer which are connected in sequence, and the image information fusion module comprises an up-sampling unit, a feature map splicing unit and a third self-attention convolution unit which are connected in sequence; obtaining a brain CT sample image; training the neural network model by taking the brain CT sample image as input and the bleeding condition of each pixel point in the brain CT sample image as a label; and adopting the trained neural network model to perform cerebral hemorrhage identification on the to-be-segmented brain CT image. According to the method, the bleeding area in the brain CT image can be accurately and efficiently segmented.

Description

technical field [0001] The present invention relates to the technical field of image segmentation, in particular to a deep learning-based cerebral hematoma segmentation method and system. Background technique [0002] Stroke is a cerebrovascular disease, mainly caused by hemorrhage caused by rupture of blood vessels in the non-external cerebral parenchyma. There are many reasons for cerebral hemorrhage, such as hypertension, hyperlipidemia, diabetes and other cardiovascular diseases. The incidence rate is extremely high in life. According to the data released by the World Health Organization, about 30 to 40% of the annual death population is caused by cerebral hemorrhage. At present, cerebral hemorrhage has become the highest mortality rate of human beings today. one of the diseases. However, there are currently few methods for quantitatively measuring the volume of suspected hematoma areas. Fast, accurate, and repeatable volume estimation is crucial for many medical diagno...

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Application Information

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IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/084G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/20221G06T2207/30101G06N3/045
Inventor 余南南于贺
Owner XUZHOU NORMAL UNIVERSITY
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