Abstract: The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for indicating whether additional medical tests are required after analysing an initial medical screening, in substantially real-time. Aspects and/or embodiments seek to provide a method and system for recommending additional medical tests, in substantially real-time, based on analysing an initial medical scan, with the use of deep learning.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: The present invention relates to a method and system that automatically classifies tissue type/patterns and density categories in mammograms. More particularly, the present invention relates to improving the quality of assessing density and tissue pattern distribution in mammography. According to a first aspect, there is provided a computer-aided method of analysing mammographic images, the method comprising the steps of: receiving a mammogram; segmenting one or more anatomical regions of the mammogram; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammogram.

Abstract: The present invention relates to a method and system that automatically determines malignancy in mammograms in parallel with a human operator. More particularly, the present invention relates to providing a reliable automated malignancy determination in parallel to a human operator to reduce the need for two human operators in a mammography analysis workflow. Aspects and/or embodiments seek to provide a method of automatically assessing mammography data in parallel with a human operator. Aspects and/or embodiments also seek to address the problems relating to providing a substantially reliable second reader to allow a single operator to analyse and diagnose mammography data.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for indicating whether additional medical tests are required after analysing an initial medical screening, in substantially real-time. Aspects and/or embodiments seek to provide a method and system for recommending additional medical tests, in substantially real-time, based on analysing an initial medical scan, with the use of deep learning.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: The present invention relates to a method and system that automatically determines malignancy in mammograms in parallel with a human operator. More particularly, the present invention relates to providing a reliable automated malignancy determination in parallel to a human operator to reduce the need for two human operators in a mammography analysis workflow. Aspects and/or embodiments seek to provide a method of automatically assessing mammography data in parallel with a human operator. Aspects and/or embodiments also seek to address the problems relating to providing a substantially reliable second reader to allow a single operator to analyse and diagnose mammography data.

Abstract: The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

Abstract: The present invention relates to a method and system that automatically classifies tissue type/patterns and density categories in mammograms. More particularly, the present invention relates to improving the quality of assessing density and tissue pattern distribution in mammography. According to a first aspect, there is provided a computer-aided method of analysing mammographic images, the method comprising the steps of: receiving a mammogram; segmenting one or more anatomical regions of the mammogram; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammogram.