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

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.

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...

AI Technical Summary

Problems solved by technology

The method based on deep learning can obtain more accurate automatic segmentation results. However, the existing heart segmentation methods based on deep learning mostly use 2D segmentation methods without considering the inter-layer context information. The inter-layer context information is important for accurate segmentation and improved segmentation. performance is valuable

Ignoring inter-layer contextual information is not in line with the actual workflow of clinicians

At the same time, due to the thick and large spacing of the scanning layers of the cardiac cine MRI image, directly using the inter-layer context information through the 3D segmentation method is not only computationally expensive but may not bring about performance improvement.

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