Lung parenchyma CT image segmentation method based on weighted full convolutional neural network

Abstract

The invention discloses a lung parenchyma CT image segmentation method based on a weighted full convolutional neural network, and belongs to the field of medical image processing. The method comprisesthe following steps: selecting a public lung data set for preprocessing, and extracting a lung parenchyma boundary in a labeled image as a semantic category; designing an improved network structure based on a standard full convolutional neural network framework, and establishing an overall structure framework of the pulmonary parenchyma segmentation convolutional neural network by using a principle that a standard path structure for encoding and decoding simultaneously comprises jump connection, expansion convolution and batch normalization; adopting a weighted loss function layer; dividing the data set; carrying out offline model training out to acquire model weight parameters; inputting a test image and outputting a segmentation result by an output layer through layer-by-layer feedforward of a network. According to an existing lung parenchyma segmentation method, a segmentation missing phenomenon is prone to occurring in a focus area in lung parenchyma, and correct segmentation of the focus area in lung parenchyma segmentation can be effectively improved through enhancement processing on important pixels.

Description

Technical field [0001] The invention relates to the field of medical image processing, in particular to a lung parenchymal CT image segmentation method based on a weighted full convolutional neural network. Background technique [0002] Relevant studies have shown that early detection and timely treatment of lung cancer can greatly improve the cure rate of lung cancer patients. CT has been proven to be an effective medical imaging technology for the diagnosis of lung diseases and is widely used in lung cancer detection and diagnosis. However, it is difficult for doctors to distinguish suspected lesion areas in a large number of CT slices with their eyes. The research and application of computer-aided diagnosis systems will help improve the accuracy and objectivity of diagnosis and reduce the workload of diagnosis. In the design of a computer-aided diagnosis system for lungs, accurate extraction of lung parenchymal regions is an important prerequisite step that affects the accurac...

AI Technical Summary

Problems solved by technology

The overall classification accuracy of the lung parenchyma is high, but some small nodules around the lung wall are often not correctly segmented

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