Computerised tomography image processing

Abstract

A method for training a machine learning image segmentation algorithm to segment structural features of a blood vessel in a computed tomography (CT) image is described herein. The method comprises receiving a labelled training set for the machine learning image segmentation algorithm. The labelled training set comprising a plurality of CT images, each CT image of the plurality of CT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding CT image of the plurality of CT images. The method further comprises training a machine learning image segmentation algorithm, using the plurality of NCT images and the corresponding plurality of segmentation masks, to learn features of the CT images that correspond to structural features of the blood vessels labelled in the segmentation masks, and output a trained image segmentation model. The method further comprises outputting the trained image segmentation model usable for segmenting structural features of a blood vessel in a CT image. Further methods are described herein for using the trained image segmentation model to segment structural features of blood vessels, and to establish the training set for training the machine learning image segmentation model. Computing apparatuses and computer readable media are also described herein.

Dependencies are inferred from the claim text.