Volpara Health Technologies Limited

Other

Ralph Highnam

John Michael Brady

Nico Karssemeijer

Martin Yaffe

Melissa Hill

Julian Marshall

Dave Murray

Lisa Renee Johnston

Seymour Francis Knowles-barley

Kaier Wang

The present invention relates to training data sets and a system and method for generating training images especially those which are medical images. Especially disclosed is a method of training a machine learning model to recognize movement of a body part in an acquired medical image The machine learning model is trained by varying/modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method including determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel.

System and method for generating training images

The present invention relates to a method and system for the transformation of raw mammograms to normalised presentation and where the pixel values are independent of imaging conditions. The performed method includes: contrast enhancement, for improved visibility of the breast tissue composition, whereby a region of the breast is segmented and a contrast-stretching algorithm applied to the segmented region to preferably create an enhanced raw image or mammogram; local 'maximum' transform, whereby a 2-dimensional first filter is designed to extract the maximum pixel value from a region of interest (ROI) to preferably create a local maximum image or map; ratio map derivation, whereby the pixel value of the ratio map measures a relative response of the said pixel to its local maximum thus capturing the difference between breast composition regardless of mammogram variations.

Method and system for image normalisation

Use of tissue composition and anthropomorphic measures in a method for the detection and quantification of arterial calcification in an organ is described for disease risk prediction and stratification. A radiographic image of an organ is transformed quantitatively to a tissue composition map indicating a total amount of organ tissue; a calcification map is generated indicating position in the tissue composition map of calcified tissue; calcification free tissue composition map is generated from the tissue composition map using the position of calcified tissue in the calcification map; a vessel map of the position of vessels in the tissue composition map is generated; and the vessel map is combined with the calcification map to generate a map of vessel calcification indicating the position of calcified vessels in the tissue composition map. Scores are based on arterial calcification in a breast which indicates disease in the breast and other organs.

Method for detection and quantification of arterial calcification

The present invention relates to a system and apparatus comprising automated analysis and feedback on soft tissue imaging quality, including analysis and feedback in real time and in conformance with key parameters and metrics which affect the quality of an image. The system is arranged to implement a method wherein method for providing a qualitative and/or quantitative assessment of an image including several steps. The steps include using an imaging apparatus to obtain at least one soft tissue image of a patient and at least one test object image; deriving a soft tissue image parameter and statistical data from image data from the soft tissue image. The system and method are characterised by employing a metric for the assessment in comparison to the parameter and statistical data, wherein the metric is obtained from the soft tissue image data, data obtained from the at least one test object image, and data from the imaging apparatus.

System and apparatus for clinical decision optimisation

A method comprises receiving image data representative of an X-ray image of at least part of a subject comprising tissue, wherein a first part of the X-ray image is directly 5 exposed and a second part of the X-ray image is representative of tissue in a region of the subject; and determining a measure of attenuation in dependence on an energy measure obtained from the first part of the X-ray image and an energy measure obtained from the second part of the X-ray image.

Method for quantification of images

The present invention relates to a method of reconstruction of an object from projections, more particularly to a quantitative reconstruction of an object from projection views of the object. For example, quantitative reconstruction of an image of a human breast from projection views generated by digital breast tomosynthesis (DBT), computed tomography (CT), or standard mammography, and use of the reconstruction to identify densest regions.

Method of reconstruction of an object from projection views

The present invention relates to an imaging technique and an imaging system and more particularly to an automatic assessment of quantitative measure(s)/properties(s) of an object wherein, for example, an imaging system is used to capture an image, following which image properties are quantified using image processing techniques. An imaging technique obtains an image of an object along a first axis, or by way of a first technique, and subsequently obtains an image of the object along a second axis, or by way of a second technique. One or more pixels from the first image are selected and designated as reference pixels. An automated comparison between corresponding regions of the first and second images are then performed which is based upon the reference pixels, so as to indicate regions of interest.

Imaging technique and imaging system

Disclosed is a method of analyzing tissue from an image comprising providing an electronic image of tissue (), determining a reference value from the image (), establishing an hrepresentation () of the image, and using the hrepresentation in analysis of the tissue to quantify the breast and compute a calibration error. Also disclosed is a system which runs an inner breast edge detection algorithm () on the electronic image to detect the inner breast edge on the image (), and refined the inner breast edge location () if a calibration error is not acceptable (). Also disclosed is automatic estimation of breast composition and temporal analysis of images.

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Wellington, New Zealand

Ralph Highnam