MRI image heart structure segmentation method based on multi-path convolutional neural network

A technology of convolutional neural network and heart structure, which is applied in the field of medical image processing, can solve problems such as high computational overhead, thick scanning layers, and large spacing, so as to improve precision and accuracy, improve segmentation performance, and have good generalization ability Effect

CN110599499AActive Publication Date: 2019-12-20SICHUAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-12-20

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Abstract

The invention relates to an MRI image heart structure segmentation method based on a multi-path convolutional neural network. The MRI image heart structure segmentation method comprises the steps: collecting cardiac movie MRI training data of a normal person and a heart patient; enabling a doctor with rich experience to manually mark the heart structure in the training data to serve as a heart segmentation marking result; training the heart region extraction model based on the training data, so as to enable the heart region extraction model to accurately extract the heart region; training theheart segmentation network according to the heart region extracted from the training data to segment each structure of the heart; and measuring the segmentation performance of the constructed heart segmentation network by taking the standard segmentation labeling result as the standard. The MRI image heart structure segmentation method adopts the heart region extraction model based on the generative adversarial network to extract the heart, so that the accuracy of heart region extraction is improved. Meanwhile, the MRI image heart structure segmentation method utilizes contextual information between adjacent layers through the multi-path convolutional neural network, so that the segmentation precision and accuracy are improved.
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Description

technical field

[0001] The invention relates to the field of medical image processing, in particular to a method for segmenting cardiac structures in MRI images based on a multi-channel convolutional neural network. Background technique

[0002] According to the statistics of the World Health Organization, cardiovascular disease is the disease with the highest mortality rate in the world, and about 19.7 million people died of cardiovascular disease in 2016. In clinical practice, cardiac function analysis plays an important role in heart disease diagnosis, risk assessment, patient management, and treatment decision-making. This is usually done with the aid of digital images of the heart to quantify global or regional cardiac function by assessing a range of clinical parameters such as ventricular volume, ejection fraction, stroke volume, myocardial mass, etc. Due to the good discrimination of soft tissues, the evaluation of left and right ventricular ejection fraction, strok...

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Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0031] The cardiac cine MRI image in the present invention refers to an image obtained by cardiac magnetic resonance cine imaging technology, which is a kind of cardiac MRI image.

[0032] The ASPP module in the present invention refers to a pyramid pooling module with dilated convolution.

[0033] Cardiac magnetic resonance cine imaging technology is a commonly used cardiac magnetic resonance imaging technology, which u...