A pulmonary nodule detection method based on a three-dimensional region generation network

Abstract

The invention discloses a pulmonary nodule detection method based on a three-dimensional region generation network, and belongs to the field of medical image detection. The method comprises the following steps of firstly, dividing and preprocessing an image data set containing pulmonary parenchyma; secondly, on the basis of the structural design of the pulmonary nodule detection network, constructing an SRI module and an SI module and using for image encoding and decoding operations; and on the training data set, adopting the cross entropy and Smart L1 function loss, and using a random gradient descent method to optimize the network; and in the test stage, inputting the preprocessed test data set into the optimized network to detect candidate pulmonary nodules, and then further determiningthe pulmonary nodules by using non-maximum suppression. According to the method, aiming at the characteristic that the pulmonary nodule size difference is large, space and channel information is fully utilized in the aspects of network construction and training, the pulmonary nodule detection capability of the network is improved, and the experiments show that good pulmonary nodule detection precision and detection effectiveness can be obtained.

Description

technical field [0001] The invention relates to the field of medical image detection, more specifically, to a method for detecting pulmonary nodules based on a three-dimensional area generation network. Background technique [0002] Lung cancer is one of the main causes of increased mortality in the population. However, because early lung cancer has no obvious symptoms, when people find it, lung cancer has already reached the advanced stage, so early detection and early treatment are of great significance to reduce the mortality of lung cancer. [0003] With the improvement of medical imaging technology, high-resolution scanning has increased the number of images, and the development of computer-aided detection technology can effectively improve the efficiency of pulmonary nodule diagnosis. However, due to the similarity between pulmonary nodules and surrounding tissues, and the large differences in the size of pulmonary nodules, the automatic detection of pulmonary nodules...

AI Technical Summary

Problems solved by technology

However, due to the similarity between pulmonary nodules and surrounding tissues, and the large differences in the size of pulmonary nodules, the automatic detection of pulmonary nodules is still a difficult task.

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