A method, system, and computer-readable medium, for detecting whether an eye blink or non-blink is captured in the image. The method includes filtering, from the image, one or more objects that are predicted to be unsuitable for determining whether an eye blink or no-blink is captured in the image, to provide a filtered image. The method also includes correlating the filtered image with a reference image, and determining, based on the correlating, whether the eye blink or non-blink is captured in the image. The eye blink is a full eye blink or a partial eye blink, and the images may be sequentially captured IR SLO images, in one example embodiment herein. Images determined to include an eye blink can be omitted from inclusion in a final (e.g., OCT) image.

Various methods and systems are provided for tracking a biopsy target across one or more images. In one example, a method includes determining a position of a biopsy target in a selected image of a patient based on an image registration process with a reference image of the patient, and displaying a graphical representation of the position of the biopsy target on the selected image.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

Systems and methods for image-based biological sample constituent analysis are disclosed. For example, image data corresponding to an image having a target constituent and other constituents may be generated and utilized for analysis. The systems and processes described herein may be utilized to differentiate between portions of image data corresponding to the target constituent and other portions that do not correspond to the target constituent. Analysis of the target constituent instances may be performed to provide analytical results.

A computer assisted surgery system comprising: a computerised surgical apparatus; and a control apparatus; wherein the control apparatus comprises circuitry configured to: receive information indicating a first region of a surgical scene from which information is obtained by the computerised surgical apparatus to make a decision; receive information indicating a second region of the surgical scene from which information is obtained by a medical professional to make a decision; determine if there is a discrepancy between the first and second regions of the surgical scene; and if there is a discrepancy between the first and second regions of the surgical scene: perform a predetermined process based on the discrepancy.

An apparatus (1) for providing image quality feedback during a medical imaging examination includes at least one electronic processor (20) programmed to: receive a live video feed (17) of a display (6) of an imaging device controller (4) of an imaging device (2) performing the medical imaging examination; extract a preview image (12) from the live video feed; perform an image analysis (38) on the extracted preview image to determine whether the extracted preview image satisfies an alert criterion; and output an alert (30) when the extracted preview image satisfies the alert criterion as determined by the image analysis.

Systems and methods relate to processing optical tomography coherence (OCT) images to predict characteristics of a treatment to be administered to effectively treat age-related macular degeneration. The processing can include pre-processing the image by flattening and/or cropping the image and processing the pre-processed image using a neural network. The neural network can include a deep convolutional neural network. An output of the neural network can indicate a predicted frequency and/or interval at which a treatment (e.g., anti-vascular endothelial growth factor therapy) is to be administered so as to prevent leakage of vasculature in the eye.

Methods are provided for dynamically visualizing information in image data of an object of interest of a patient, which include an offline phase and an online phase. In the offline phase, first image data of the object of interest acquired with a contrast agent is obtained with an interventional device is present in the first image data. The first image data is used to generate a plurality of roadmaps of the object of interest. A plurality of reference locations of the device in the first image data is determined, wherein the plurality of reference locations correspond to the plurality of roadmaps. In the online phase, live image data of the object of interest acquired without a contrast agent is obtained with the device present in the live image data, and a roadmap is selected from the plurality of roadmaps. A location of the device in the live image data is determined. The reference location of the device corresponding to the selected roadmap and the location of the device in the live image data is used to transform the selected roadmap to generate a dynamic roadmap of the object of interest. A visual representation of the dynamic roadmap is overlaid on the live image data for display. In embodiments, the first image data of the offline phase covers different of phases of the cardiac cycle of the patient, and the plurality of roadmaps generated in the offline phase covers the different phases of the patient's cardiac cycle. Related systems and program storage devices are also described and claimed.

This disclosure proposes to speed up computation time of a convolutional neural network (CNN) by leveraging information specific to a pre-defined region, such as a breast in mammography and tomosynthesis data. In an exemplary embodiment, a method for an image processing system is provided, comprising, generating an output of a trained convolutional neural network (CNN) of the image processing system based on an input image, including a pre-defined region of the input image as an additional input into at least one of a convolutional layer and a fully connected layer of the CNN to limit computations to input image data inside the pre-defined region; and storing the output and/or displaying the output on a display device.