Quantification of Lung Fibrosis

A computer-implemented method of computing a lung fibrosis metric for a patient, the method comprising:

• receiving initial lung imaging data for the patient;
• and
• applying a trained neural network lung segmentation model to the initial lung imaging data to generate lung segmentation data, the lung segmentation data indicating which portions of the lung imaging data relate to lung features of the patient, the lung segmentation data comprising a set of feature vectors for portions of a three-dimensional volume;
• applying the lung segmentation data to the lung imaging data to produce modified lung imaging data;
• applying a trained neural network lung fibrosis model to the modified lung imaging data to generate fibrosis segmentation data, the fibrosis segmentation data indicating which portions of the modified lung imaging data relate to fibrosis features, the fibrosis segmentation data comprising a set of feature vectors for portions of a three-dimensional volume;
• processing the fibrosis segmentation data in combination with the lung segmentation data to generate labelled voxel data;
• computing a fibrosis volume metric for the patient using the labelled voxel data, the fibrosis volume metric representing a proportion of lung volume that exhibits fibrosis;
• and
• providing the fibrosis volume metric as the lung fibrosis metric for the patient.

A computer-implemented method of configuring a lung fibrosis metric system, the method comprising:

• obtaining lung segmentation training data comprising lung imaging data with lung segmentation annotations, the lung segmentation data comprising a set of feature vectors for portions of a three-dimensional volume;
• and
• training a neural network lung segmentation model based on the lung segmentation training data to produce a set of trained lung segmentation model parameters, said training comprising optimising a loss function, the loss function being computed based on a comparison of a ground-truth lung segmentation annotation and a predicted lung segmentation annotation from the neural network lung segmentation model;
• obtaining fibrosis training data comprising lung imaging data with fibrosis segmentation annotations;
• and
• training a neural network lung fibrosis model based on the fibrosis training data, said training comprising optimising a loss function, the loss function being computed based on a comparison of a ground-truth fibrosis segmentation annotation and a predicted fibrosis annotation from the neural network lung fibrosis model, the predicted fibrosis annotation being computed by:
• computing lung segmentation data using the lung segmentation model instantiated with the trained lung segmentation model parameters as applied to a sample from the lung imaging data;
• pre-processing the sample from the lung imaging data using the lung segmentation data to generate a modified sample;
• and
• applying the lung fibrosis model in a training mode to the modified sample to generate the predicted fibrosis annotation, the predicted fibrosis annotation being generated from set of feature vectors for portions of a three-dimensional volume that are output by the lung fibrosis model in the training mode.