Provided are ex vivo systems and methods of predicting the response of a drug or other agent on a tissue. In some embodiments, the systems and methods comprise cutting a tissue into tissue fragments, adding a drug or other agent to the tissue fragments based on an estimated tumor content, and performing an ex vivo measurement on the tissue fragments.

Embodiments consistent with the present disclosure provide systems, methods, and devices for providing wound capturing guidance. In one example, consistent with the disclosed embodiments, an example system may: display, on a mobile device, a user interface configured to guide a patient through one or more steps for performing a medical action, the plurality of steps including at least: using at least one item of a medical kit; and capturing at least one image of at least part of the at least one item of the medical kit using at least one image sensor associated with the mobile device. The example system may also: detect a failure to successfully complete the medical action; select from one or more alternative reactions, a reaction to the detected failure likely to bring a successful completion of the medical action; and provide instructions associated with the selected reaction.

A non-transitory computer readable medium storing data and computer implementable instructions that, when executed by at least one processor, cause the at least one processor to perform operations for generating cross section views of a wound, the operations including receiving 3D information of a wound based on information captured using an image sensor associated with an image plane substantially parallel to the wound; generating a cross section view of the wound by analyzing the 3D information; and providing data configured to cause a presentation of the generated cross section view of the wound.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Disclosed are systems and methods for extracting high resolution oculometric parameters and eye movement parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius). The oculometric parameters are generated at a first temporal resolution. The oculometric parameters are up sampled to increase the temporal resolution to a second temporal resolution. The oculometric parameters are then processed to generate various eye movement parameters such as blink parameter, pupil response parameter, saccade parameter, anti-saccade parameter, fixation parameter, or smooth pursuit parameter. The oculometric parameters are synchronized with a video stimulus presented on a user device prior to generating the eye movement parameters.

According to embodiment, an image processing apparatus comprising a specifying unit and a display controller. The specifying unit that specifies an acquisition position of an indicator relating to blood flow on a blood vessel-containing image collected by a medical image diagnostic apparatus. The display controller that displays the acquisition position on the blood vessel-containing image and displays the indicator on a display unit in association with the acquisition position.

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images includes accessing an identification of a target lesion in one or more cross-sectional images at a computing system, determining a computer segmentation of the target lesion, and, based upon the computer segmentation, automatically determining one or more lesion metrics for the target lesion. The one or more lesion metrics includes at least a target lesion length. The target lesion length includes a short axis measurement when the target lesion is a lymph node, and the target lesion length includes a long axis measurement when the target lesion is not a lymph node. The computer-implemented method further includes generating a summary display including at least the target lesion length.

The disclosed method encompasses using an augmented reality device to blend in augmentation information including for example atlas information. The atlas information may be display separately from or in addition to a patient image (planning image). In order to display the atlas information in a proper position relative to the patient image, data the two data sets are registered to one another. This registration can serve for generating a diversity of atlas-based image supplements, for example alternatively or additionally to the foregoing for displaying a segmentation of the patient image in the augmented reality image. The disclosed method is usable in a medical environment such as for surgery or radiotherapy.

The present invention relates to deep learning for automated segmentation of a medical image. More particularly, the present invention relates to deep learning for automated segmentation of anatomical regions and lesions in mammography screening and clinical assessment. According to a first aspect, there is provided a computer-aided method of segmenting regions in medical images, the method comprising the steps of: receiving input data; analysing the input data by identifying one or more regions; determining one or more characteristics for the one or more regions in the input data; and generating output segmentation data in dependence upon the characteristics for the one or more regions.

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.