Generating ground truth annotated dataset for analysing medical images

A machine-readable, non-transitory memory, having stored thereon one or more programs programmed to cause a processor to compute at least the following:

• to images of a plurality of medical images, to apply a trained segmentation model to generate first segmentation masks for lesions of the medical images, a segmentation mask being a designation of an area of an image corresponding to a lesion shown in the image, a segmentation model being an artificial intelligence model trained to identify segmentation masks corresponding to lesions in medical images;
• to apply a geometric filter to evaluate the generated first segmentation masks based on geometric properties of the respective one or more lesions in the images;
• to combine into a training set for training of a segmentation model the evaluated first segmentation masks and their corresponding images based at least in part on meeting the geometric filter;
• and
• to train a regression function based on ground truth values for relative area fraction, convexity, elliptical aspect ratio, and elliptical proximity for lesions in medical images, to validate if the generated segmentation mask corresponds to the one or more lesions in the corresponding medical image.

A method, comprising the steps of:

• to images of a plurality of medical images, applying a trained segmentation model to generate first segmentation masks for lesions of the medical images, a segmentation mask being a designation of an area of an image corresponding to a lesion shown in the image, a segmentation model being an artificial intelligence model trained to identify segmentation masks corresponding to lesions in medical images;
• applying a geometric filter to evaluate the generated first segmentation masks based on geometric properties of the respective one or more lesions in the images;
• and
• combining into a training set for training of a segmentation model the evaluated first segmentation masks and their corresponding images based at least in part on meeting the geometric filter;
• wherein in the applied geometric filter includes at least four of:
• determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the corresponding medical image;
• a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the corresponding medical image;
• a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion;
• and
• an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding to a lesion.