Pulmonary nodule analysis method and device

Abstract

The invention discloses a pulmonary nodule analysis method and device. The method comprises obtaining a lung nodule image of the lung of the patient and a relative position of the lung nodule relativeto the intrapulmonary space in the lung nodule image, and a segmentation image of the lung according to the relative position of the lung nodule relative to the intrapulmonary space; determining TheROI of the pulmonary nodule in the lung nodule image, and the ROI of the pulmonary nodule, the relative position of the lung nodule relative to the intrapulmonary space, and inputting feature vectorsof the image of pulmonary nodule to a preset feature extraction neural network model. Compared with a traditional doctor diagnosis mode, the diagnosis error rate caused by the horizontal difference ofdoctors can be reduced, and therefore the accuracy of pulmonary nodule analysis is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of machine learning, and in particular to a method and device for analyzing pulmonary nodules. Background technique [0002] With the increasingly serious environmental pollution, more and more diseases show a trend of high outbreak rate. The development of modern medical technology is very mature, and doctors can diagnose various diseases with medical knowledge and medical experience. That is to say, most of the determination of diseases in the prior art depends on the diagnosis of doctors. However, because the medical level in various regions is very inconsistent, and the level of personal experience of doctors is also uneven, the traditional method of diagnosing diseases by doctors is vulnerable. The image of the regional medical level and the doctor's personal experience level leads to the problem of large diagnostic errors. [0003] Taking pulmonary nodules as an example, doctors...

AI Technical Summary

Problems solved by technology

That is to say, most of the determination of diseases in the prior art depends on the diagnosis of doctors. However, because the medical level in various regions is very inconsistent, and the level of personal experience of doctors is also uneven, the traditional method of diagnosing diseases by doctors is vulnerable. Imaging of regional medical level and doctor's personal experience level leads to large diagnostic errors

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