CPC G06T 7/11 (2017.01) [G06T 7/149 (2017.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01)] | 14 Claims |
1. A method for assessing pulmonary function, comprising:
receiving medical image data that includes image data of at least one lung;
segmenting image data of the at least one lung from other image data;
modeling the segmented image data using a model with a central-symmetric system of pixel-pixel interactions; and
classifying, using a neural network, pulmonary function as a first state or a second state based at least in part on the model;
wherein modeling the segmented image data includes designating neighborhood sets for a plurality of different radii and determining Gibbs energy for each of the plurality of different radii; and
wherein the classifying uses a plurality of neural networks, each receiving Gibbs energy from different radii as input, and a fusion neural network which uses the output of the plurality of neural networks as input.
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