Abstract: A method for providing a lung disease risk measure in a Computer Aided Diagnosis system is described. The method comprising the steps of: receiving a plurality of inputs for a subject, each input comprising at least one image showing all or part of the lungs of a patient and a time stamp for the image, where the inputs are obtained at varying intervals; analysing the inputs to assess temporal changes in the images using at least one of an input data encoder and a time stamp encoder; inputting the output of at least one of the encoders to a score calculator to calculate a risk score; outputting the risk score indicating the lung disease risk for the subject. A Computer Aided diagnosis system for implementing the method is also described.

Abstract: A Computer Aided Diagnosis, CADx, system (200) is described that comprises: at least one input (210, 212, 214) configured to provide at least one input medical image; and a CADx processing engine (220) configured to receive and process the at least one input medical image and produce at least one CADx score. A CADx score mapping circuit is operably coupled to the CADx processing engine (220) and configured to: map the at least one CADx score to a risk adjusted virtual score; and generate an output (235) of at least the risk adjusted virtual score associated with the processed at least one input medical image. The at least one CADx score and the risk adjusted virtual score correspond to an equivalent risk of condition or disease associated with a patient.

Abstract: A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

Abstract: A method for analysing images in a computer aided diagnosis system (CADx) to provide a first image analysis score and a second image analysis score for an image is described. The method comprising; receiving an input comprising at least one input image showing all or part of the lungs of a subject; analysing the input to calculating a first image analysis value and a second image analysis value for the input and processing the calculated values to generate corresponding first image analysis and second image analysis scores and outputting at least one of the first image analysis score and the second image analysis score for the subject. A computer aided diagnosis system (CADx) and a method of training a computer aided diagnosis system are also described.

Abstract: A CADx system for analysing medical images and determining if the images are acceptable for analysis, by determining if the images contain out-of-distribution input data is described. The CADx system comprises: an input circuit for receiving at least one medical image; a gatekeeper circuit for determining if the at least one received medical image contains out-of-distribution input data and so does not meet the requirements of the CADx system for acceptable images; and an output circuit to produce an output that is either a determination that the at least one received medical image contains out-of-distribution data and is not suitable for analysis by the CADx system, or a determination that the medical image is acceptable.

Abstract: A method for providing a lung disease risk measure in a Computer Aided Diagnosis system is described. The method comprising the steps of: receiving a plurality of inputs for a subject, each input comprising at least one image showing all or part of the lungs of a patient and a time stamp for the image, where the inputs are obtained at varying intervals; analysing the inputs to assess temporal changes in the images using at least one of an input data encoder and a time stamp encoder; inputting the output of at least one of the encoders to a score calculator to calculate a risk score; outputting the risk score indicating the lung disease risk for the subject. A Computer Aided diagnosis system for implementing the method is also described.

Abstract: A method for providing a lung disease risk measure in a Computer Aided Diagnosis system is described. The method comprises the steps of: receiving an input comprising at least one input image showing all or part of the lungs of a patient; analysing the input to identify a feature descriptor comprised of at least one feature group, where each feature group comprises at least one feature computed from the input image; calculating a score explanation factor for each feature group, and an overall disease risk score for the patient's risk of lung disease from the feature descriptor; outputting the overall disease score risk score and a corresponding score explanation factor for each feature group. A computer aided diagnosis system is also described, along with a method for training a computer aided diagnosis system.

Abstract: A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

Abstract: A Computer Aided Diagnosis, CAD, lung disease risk measure system is described that comprises: an input circuit configured to receive at least one input medical image of a patient in which the patient's lungs are visible. A smoking history system is operably coupled to the input circuit and configured to receive and analyse the at least one input medical image of a patient and determine an equivalent smoking history of the patient based on the at least one input medical image and output a smoking history parameter based on the determined equivalent smoking history of the patient.

Abstract: A quantification system (300) is described that includes: at least one input (310) configured to provide at least one input medical image and provide a location of interest in the at least one input medical image; and a mapping circuit (325). The mapping circuit (325) is configured to: compute a direct quantification result from the location of interest of the at least one input medical image to a quantification of interest and output a direct first quantification result therefrom; generate a saliency map as part of the computation of the direct quantification result; and derive a segmentation from the saliency map, such that the segmentation independently generates a second quantification result that is within a result range of the direct first quantification result.

Abstract: A quantification system (300) is described that includes: at least one input (310) configured to provide at least one input medical image and provide a location of interest in the at least one input medical image; and a mapping circuit (325). The mapping circuit (325) is configured to: compute a direct quantification result from the location of interest of the at least one input medical image to a quantification of interest and output a direct first quantification result therefrom; generate a saliency map as part of the computation of the direct quantification result; and derive a segmentation from the saliency map, such that the segmentation independently generates a second quantification result that is within a result range of the direct first quantification result.

Abstract: A method and apparatus for assessing image registration. The method comprises obtaining image datasets for the first and second medical images and registration data representing the registration from the first medical image to the second medical image, collating use-case information for the image registration, deriving a set of at least one measurement and assessment criteria therefor based at least partly on the collated use-case information, performing the at least one measurement on at least one of the obtained image datasets and the obtained registration data to derive at least one measurement value, applying the assessment criteria for the at least one measurement to the at least one measurement value to derive at least one assessment result, and outputting an indication of the at least one assessment result.

Abstract: A Computer Aided Diagnosis, CAD, training system (400) is described for training a CAD device (310) to receive and process at least one input medical image and produce an output that indicates a probability of a medical condition of a patient. The CAD device (310) comprises: an input circuit (305) configured to receive and assemble training data that comprises: medical data of patients that have been identified as having at least one medical condition, and medical data of patients that have been identified as not having the at least one medical condition.

Abstract: A Computer Aided Diagnosis, CADx, system (200) is described that comprises: at least one input (210, 212, 214) configured to provide at least one input medical image; and a CADx processing engine (220) configured to receive and process the at least one input medical image and produce at least one CADx score. A CADx score mapping circuit is operably coupled to the CADx processing engine (220) and configured to: map the at least one CADx score to a risk adjusted virtual score; and generate an output (235) of at least the risk adjusted virtual score associated with the processed at least one input medical image. The at least one CADx score and the risk adjusted virtual score correspond to an equivalent risk of condition or disease associated with a patient.

Abstract: A method and apparatus for selecting a subset of atlases from a set of candidate atlases. The method comprises: for each candidate atlas n within the set of N candidate atlases, computing an auto-contouring performance measure in relation to each training atlas t within a subset of a set of T training atlases; for each pair combination of candidate atlas n and training atlas t for which an auto-contouring performance measure has been computed, deriving a score based on the computed auto-contouring performance measures; for each candidate atlas n within the set of N candidate atlases, aggregating the derived scores for each pair combination comprising that candidate atlas n; and selecting a subset of M candidate atlases from the set of N candidate atlases based on the aggregated scores therefor.

Abstract: A method of generating image alignment data for medical images. The method comprises determining prospective image pair registration connections, determining registration weighting values for the determined prospective image pair registration connections based at least partly on intra-image attribute data, determining optimal registration paths for image pairs based at least partly on the registration weighting values for the prospective image pair registration connections, and generating image alignment data for the determined optimal registration paths.

Abstract: A method and apparatus for delineating an object within a volumetric medical image. The method comprises obtaining auto-generated contour data for the object within the volumetric medical image, the auto-generated contour data defining a set of auto-generated contours forming a delineation structure for the object, selecting a subset of auto-generated contours for manual editing, and identifying the selected subset of auto-generated contours to a user. In some examples, the method further comprises presenting at least the selected subset of auto-generated contours to the user, receiving user feedback for at least one auto-generated contour, deriving a full set of contours forming a revised delineation structure for the object based at least partly on an interpolation of the auto-generated contour(s) for which user feedback was received, and storing contour data defining the derived full set of contours forming the revised delineation structure for the object within at least one data storage device.

Abstract: A method and system are provided for creating and simultaneously displaying medical scan images, from each of first (780) and second (530) medical scan datasets, obtained by scanning a 3-dimensional (3-D) object with different scanning modalities. A first image (410) is derived from the first (780) dataset, the first image (410) lying in a first plane corresponding to an acquisition plane of the first (780) dataset. A second image (420) is obtained from the second dataset (530), the second image also lying in the first plane. The second image may be obtained by re-slicing the second data set. One or both medical scan datasets may be multivolume datasets. The invention may improve viewing resolution and/or speed, when viewing a multi-series MRI scan together with a CT and/or a PET scan.

Abstract: A method and apparatus for assessing image registration. The method comprises obtaining image datasets for the first and second medical images and registration data representing the registration from the first medical image to the second medical image, collating use-case information for the image registration, deriving a set of at least one measurement and assessment criteria therefor based at least partly on the collated use-case information, performing the at least one measurement on at least one of the obtained image datasets and the obtained registration data to derive at least one measurement value, applying the assessment criteria for the at least one measurement to the at least one measurement value to derive at least one assessment result, and outputting an indication of the at least one assessment result.

Abstract: A method and apparatus for selecting a subset of atlases from a set of candidate atlases. The method comprises: for each candidate atlas n within the set of N candidate atlases, computing an auto-contouring performance measure in relation to each training atlas t within a subset of a set of T training atlases; for each pair combination of candidate atlas n and training atlas t for which an auto-contouring performance measure has been computed, deriving a score based on the computed auto-contouring performance measures; for each candidate atlas n within the set of N candidate atlases, aggregating the derived scores for each pair combination comprising that candidate atlas n; and selecting a subset of M candidate atlases from the set of N candidate atlases based on the aggregated scores therefor.