Two-way cross-connected convolutional neural network for image segmentation

Abstract

A two-way cross connection convolutional neural network for image segmentation performs accurate segmentation while executing different interest targets in a multi-modal medical image, and achieves effective extraction of different interest targets by introducing two different network branches and a new cross jump connection in an existing BiO-Net segmentation network. The segmentation experiment based on the disclosed eye fundus image shows that the optic disc and optic cup areas in the eye fundus image can be effectively extracted, and the segmentation performance superior to that of existing networks such as U-Net and BiO-Net is obtained.

Description

technical field [0001] The invention specifically relates to the technical field of image segmentation and target detection, in particular to a convolutional neural network with bidirectional cross-connection for image segmentation. Background technique [0002] Image segmentation is a technology that divides the entire image into several independent local areas according to imaging characteristics such as gray distribution and tissue contrast. This technology can be used for tasks such as the understanding and analysis of medical images, the detection and location of lesions, and the measurement and evaluation of morphological characteristics, so it has important clinical diagnosis and academic research value. Based on this, a large number of image segmentation algorithms have been proposed. These algorithms can be roughly divided into unsupervised and supervised segmentation algorithms depending on the image evaluation strategy. Unsupervised segmentation algorithms usual...

AI Technical Summary

Problems solved by technology

However, although this U-Net network has good segmentation performance, it is difficult to deal with the boundary area of ​​the target and has a large boundary detection error because (a) the network uses image downsampling operations many times, although speeding up The detection efficiency of convolution features will greatly reduce the image resolution, resulting in the blurring of the target boundary and the loss of a large amount of texture information; (b) the segmentation network only uses one-way jump connections to establish the connection between the encoding and decryption convolution modules , which is not conducive to the detection and integration of multi-level and multi-dimensional image information

In order to overcome the shortcomings of the U-Net network, various improvements have been made to construct networks such as M-Net, BiO-Net and U-Net++; however, these networks use fewer jump connections, which are not enough There are many deficiencies in alleviating the information loss problem caused by multiple downsampling

Images