Abstract: The present disclosure relates to a method and system for diagnosing blood vessel obstruction, such as would occur in deep vein thrombosis (DVT). More specifically, the present disclosure relates to a software tool for use with sensor hardware to allow users to perform standardised and repeatable testing of patients to assist with diagnosis of deep vein thrombosis or related conditions. The present disclosure further relates to an apparatus and method for conducting the diagnosis. The apparatus includes an imaging probe and a computing device, where the computing device is configured to assist the user in placing the imaging probe on a blood vessel and moving the imaging probe between predetermined landmarks along the blood vessel.

Abstract: The present invention relates to a method and system of determining a measure of confidence for a trained deep learning model. Further, the present invention relates to a method and system of predicting a measure of confidence for a trained deep learning model using the latent variables of the trained deep learning network. Aspects and/or embodiments recite a method and/or system that, by modelling a portion of the latent space with probabilistic techniques, allows network prediction to be sampled and tested for robustness in order to derive a measure of certainty/uncertainty. This measure of certainty/uncertainty, according to aspects and/or embodiments, can be used to reject network inputs that will lead to outputs or predictions below a predetermined decision confidence threshold.

Abstract: The present invention relates to a method and system for diagnosing blood vessel obstruction, such as would occur in deep vein thrombosis (DVT). More specifically, the present invention relates to a software tool for use with sensor hardware to allow users to perform standardised and repeatable testing of patients to assist with diagnosis of deep vein thrombosis or related conditions. The present invention further relates to an apparatus and method for conducting the diagnosis.

Abstract: In a method and apparatus for automatic background region selection for lesion segmentation in medical images, a patient medical image dataset is loaded into a computer and an image region is delineated to obtain a segmentation containing a lesion. A background region is created from the segmentation representing the patient organ.

Abstract: In a method and apparatus for automatic background region selection for lesion segmentation in medical images, a patient medical image dataset is loaded into a computer and an image region is delineated to obtain a segmentation containing a lesion. A background region is created from the segmentation representing the patient organ.