An image analysis method based on a recurrent neural network

Abstract

The invention discloses an image analysis method based on a recurrent neural network, and the method comprises the steps: building a plurality of first two-dimensional axial slice images based on an original three-dimensional image; Carrying out convolution operation on the plurality of two-dimensional axial slice images to obtain a high-resolution characteristic image, stacking the characteristicimage into a three-dimensional characteristic image, and then cutting the three-dimensional characteristic image into an axial view, a sagittal view and a coronal view; Processing the axial view through a sequential learning network to generate an axial sequential learning feature map; Processing the sagittal view by expanding the residual network to generate a sagittal learning feature map; Processing the coronal view by expanding the residual network to generate a coronal learning feature map; Creating a first three-dimensional body based on the sagittal learning feature map, and cutting the first three-dimensional body into a plurality of second two-dimensional axial slices; Creating a second three-dimensional body based on the coronal learning feature map, and cutting the second three-dimensional body into a plurality of third two-dimensional axial slices; Cascading the axial sequence learning feature map, the second plurality of two-dimensional axial slices and the third plurality of two-dimensional axial slices to form cascade feature mapping; Applying a convolution operation to the cascade feature mapping to obtain a fused multi-view feature; And combining the fused multi-view feature with the high-resolution feature map to carry out image segmentation.

Description

technical field [0001] The present invention relates to the technical field of image processing. Specifically, the present invention relates to a method for analyzing cardiac MRI images based on a recurrent neural network. Background technique [0002] Time-lapse gadolinium-enhanced cardiac magnetic resonance imaging (LGE-CMRI) has been used to acquire data in patients with atrial fibrillation (AF) to detect native and post-ablative therapeutic scarring in the thin-walled left atrium (LA). The technique is based on the different rates of metabolism of gadolinium contrast media between healthy and scar tissue and the kinetics of gadolinium contrast media. Hyperenhanced regions in time-lapse gadolinium-enhanced cardiac MRI images reflect scar tissue traits, whereas healthy atrial myocardium is 'null'. Time-lapse gadolinium-enhanced cardiac MRI can help stratify the diagnosis and treatment of patients, guide the ablation treatment plan and predict the probability of treatment...

AI Technical Summary

Problems solved by technology

For most previous studies, manual segmentation of the left atrium and pulmonary veins has been achieved, although this is time-consuming, subjective and lacks reproducibility

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