Lung tissue image segmentation method based on deep learning

Abstract

The invention provides a lung tissue image segmentation method based on deep learning, and belongs to the technical field of medical image segmentation. The lung tissue image segmentation method comprises the steps that an X-ray chest radiograph image is input into a segmentation model, the segmentation model is obtained through training of multiple sets of training data, and each set of trainingdata in the multiple sets of training data comprises the X-ray chest radiograph image and a corresponding gold standard used for identifying lung tissue; and output information of the model is obtained, and the output information comprises a segmentation result of the lung tissue in the X-ray chest radiography image. According to the lung tissue image segmentation method, the segmentation of the lung tissue of the X-ray chest radiography is realized through an improved Deeplabv3+ deep learning method, and the problems of local convergence and false positive segmentation when the lung tissue issegmented by using a traditional method are solved; the lung tissue image segmentation method respectively obtains 95.3% of MIoU and 94.8% of MIoU on the public data set and the pneumoconiosis data set; and the false positive problem of the FCN network is solved, and the segmentation accuracy of ribs at the thoracic diaphragm angle and on the X-ray chest radiography in the SCAN network method isimproved.

Description

technical field [0001] The invention relates to the technical field of medical image segmentation, in particular to a deep learning-based lung tissue image segmentation method that improves the accuracy of lung tissue segmentation and reduces the impact of noise on pneumoconiosis classification and identification. Background technique [0002] With the diversification and maturity of medical imaging technology, medical image research has become one of the hot research fields. Based on the multiplicity and diversity of lung diseases, computer-aided diagnosis has become an important means of clinical treatment. Therefore, high-accuracy technical support is essential. At present, lung tissue segmentation techniques are roughly divided into three categories: traditional methods, traditional machine learning methods, and deep learning methods. The traditional method for segmenting lung tissue has high requirements on the data set, not only the strong boundary information of the ...

AI Technical Summary

Problems solved by technology

However, purely from the visualization results, for images with dissimilar shapes in the database, the segmentation of the thoracophrenic angle and lung apex still has the problem of inaccurate segmentation.

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