Computerised tomography image processing

A method for training a classification algorithm to identify structural features of a blood vessel in a non-contrast computed tomography (NCT) image, the method comprising:

• receiving a labelled training set for the classification algorithm, the labelled training set comprising:
• a plurality of NCT images, each NCT image of the plurality of NCT images showing a targeted region of a subject, the targeted region including at least one blood vessel;
• and
• a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding NCT image of the plurality of NCT images;
• dividing each NCT image and corresponding segmentation mask into sub-volumes, each sub-volume of the corresponding segmentation mask labelling at least one feature in a corresponding sub-volume in the corresponding NCT image;
• extracting, from each sub-volume of the NCT image, radiomic feature values for a set of radiomic features;
• training a classification algorithm, using the extracted radiomic feature values for each sub-volume of each NCT image and the corresponding sub-volume of the corresponding segmentation masks, to learn features of the NCT images that correspond to structural features of the blood vessels labelled in the segmentation masks, and output a trained classification model;
• outputting the trained classification model usable for identifying structural features of a blood vessel in an NCT image.

A method for identifying structural features of a blood vessel from a non-contrast computed tomography (NCT) image, the NCT image showing a targeted region of a subject, the targeted region including at least one blood vessel, the method comprising:

• for each of a plurality of sub-volumes of the NCT image:
• sampling radiomic feature values for a set of radiomic features from the sub-volume;
• classifying the sub-volume as belonging to a structural feature of a set of structural features based on the sampled radiomic feature values;
• and
• identifying, from the classifications of the plurality of sub-volumes of the NCT image, structural features of a blood vessel shown in the targeted region.

A method for establishing a labelled training set for training a machine learning image segmentation algorithm or a classification algorithm to identify structural features of a blood vessel in a non-contrast computed tomography (NCT) image, the method comprising:

• receiving a plurality of NCT images, each NCT image showing a targeted region of a subject, the targeted region including at least one blood vessel;
• receiving a plurality of contrast computed tomography (CCT) images, each CCT image corresponding to an NCT image of the plurality of NCT images and showing the corresponding targeted region of the subject;
• adapting the plurality of CCT images to substantially align with the corresponding NCT images;
• and
• segmenting the plurality of adapted CCT images to generate a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of the at least one blood vessel in the corresponding adapted CCT image;
• wherein the labelled training set includes pairs of NCT images and segmentation masks, each pair comprising a segmentation mask and the NCT image to which the adapted CCT image substantially aligns.

A method for training a generative adversarial network (GAN) to generate a pseudo-contrast computed tomography (PCT) image from a non-contrast computed tomography (NCT) image, the GAN comprising a generator network and a discriminator network, the method comprising:

• receiving a training set comprising:
• a plurality of NCT images, each NCT image of the plurality of NCT images showing at least one anatomical structure;
• and
• a plurality of contrast computed tomography (CCT) images, each CCT image showing at least one anatomical structure;
• training the GAN, wherein training the GAN comprises:
• training the generator network, using the plurality of NCT images and feedback from the discriminator network, to generate PCT images;
• training the discriminator network, using the generated PCT images and the plurality of CCT images, to classify received images as PCT images or CCT images and to provide feedback to the generator network;
• and
• outputting a trained generator model to translate an input NCT image to a PCT image showing at least one anatomical structure.

A method for identifying anatomical structures in a non-contrast computed tomography (NCT) image, the method comprising:

• providing the NCT image to a trained generator model, the trained generator model, the generator model trained as part of a generative adversarial network, the generator model trained to translate an input NCT image to a pseudo-contrast computed tomography (PCT) image showing at least one anatomical structure;
• and
• generating, using the trained generator model, a PCT image corresponding to the provided NCT image;
• and
• identifying, from the PCT image, structural features of the at least one anatomical structure.