Method for extracting tail end bronchial tree from lung CT image
A CT image and bronchial tree technology, applied in the field of lung CT image processing, to achieve high sensitivity, reduce system complexity, and quickly identify and locate the effect
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[0012] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.
[0013] Such as figure 1 As shown, a method for extracting terminal bronchial tree from lung CT images includes the following steps:
[0014] 1) Prepare initial data: The initial data includes lung texture CT image patches used for training, verification and testing, corresponding geometric information image patches and corresponding category labels.
[0015] 2) Construction of convolutional neural network: Based on the idea of skip structure in convolutional neural network, an 18-layer convolutional neural network is constructed.
[0016] 3) training based on the convolutional neural network obtained in step (2);
[0017] More specifically, the volume base layer size of the convolutional neural network is 96 con...
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