CT image kidney segmentation algorithm based on residual double-attention deep network

Abstract

The invention discloses a CT image kidney segmentation algorithm based on a residual double-attention deep network. According to the method, the advantage that the residual error unit can repeatedly utilize the features and the excellent feature learning capability of the double attention mechanism are combined; a residual double attention module is designed; and a residual double attention moduleis used as a basic module to construct a U-shaped deep network segmentation model, and a loss function for segmentation is designed at the same time, so that the U-shaped deep network segmentation model can pay more attention to kidney region features, can effectively cope with shape changes of the kidney with cystic lesions, and can maintain robustness for the shape changes of the kidney with cystic lesions. Therefore, the boundary of the kidney area is accurately positioned, and automatic segmentation of the kidney area in the CT image is achieved, and a good segmentation effect is achieved.

Description

technical field [0001] The invention relates to the technical field of data information processing, in particular to a CT image kidney segmentation algorithm based on a residual dual attention deep network. Background technique [0002] In clinical applications, kidney segmentation is very important for disease diagnosis, functional assessment and treatment decision-making. The early segmentation work was manually outlined by experienced doctors. This segmentation method is highly subjective, inefficient, and the segmentation results cannot be reproduced. It has been unable to meet the clinical requirements very well, and has been gradually eliminated in practical applications. With the continuous development of science and technology, it is possible to use computer technology to achieve medical image segmentation, and researchers have begun to explore automatic segmentation methods. However, there are some difficulties in accurately and reliably segmenting kidneys in CT im...

AI Technical Summary

Problems solved by technology

However, due to the variety of shapes and the complexity of the anatomical structure of human kidneys, cystic lesions can also cause substantial changes in the shape of the kidneys, which makes the automatic segmentation of kidneys in CT images, especially cystic kidneys, posing many challenges.

Some existing fully convolutional networks, such as VGG-based fully convolutional networks, cannot locate the boundaries of the kidney region well

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