A model dataset is generated based on first image data. The model dataset and second image data map at least a common part of an examination region at a second detail level. The model dataset and the second image data are pre-aligned at a first detail level below the second detail level based on first features that are mapped at the first detail level in the model dataset and the second image data and/or an acquisition geometry of the second image data. The model dataset and the second image data are registered at the second detail level based on second features that are mapped at the second detail level in the model dataset and the second image data. The second class of features is mappable at the second detail level or above. The registered second image data and/or the registered model dataset is provided.

Systems and methods related to estimating the distance of a body structure from a medical device are disclosed. An example method includes illuminating the body structure with a light source of a medical device, capturing a first input image of the body structure with a digital camera positioned on the medical device, representing the first image with a first plurality of pixels, wherein the first plurality of pixels includes one or more pixels displaying a local intensity maxima, defining a first pixel group from the one or more pixels displaying a local intensity maxima, wherein the first pixel group corresponds to a plurality of surface points of the body structure and wherein the first pixel group further includes a first image intensity. The method further includes calculating a relative distance from the digital camera to a first surface point of the plurality of surface points.

A method to suppresses speckle noise in medical ultrasound images includes ultrasound envelope image matrix A formed from the medical ultrasound images and segmented into overlapping segments, to form a sub-matrix B for each overlapping segment. A Hermitian covariance matrix C is formulated from column vectors Z. A global covariance matrix G is formed by averaging the C. A Lanczos decomposition is applied to the G to generate an orthonormal vector matrix composed of orthonormal vectors. A tridiagonal matrix H is generated. The orthonormal vectors are sorted based on magnitude of each column. An orthogonal projection matrix P.sub.orth is formed based on the orthonormal vectors. An estimated vector signal {circumflex over (Z)} is obtained by projecting Z by P.sub.orth. An estimated despeckled segment is formed from the {circumflex over (Z)}. An estimated despeckled ultrasound image is reconstructed by averaging each pixel by the number of segment updates.

A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.

Disclosed herein is a method that comprises obtaining an image of a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; subjecting the image to a low pass filter to increase contrast in portions of the network sections; computing a local mean of visible light intensity at each pixel that is present in the image; calculating a visible light intensity difference between each pixel and the local mean of visible light intensity and producing a differentiated image using this calculation; creating a base image of the differentiated image; where the base image comprises a hand segmented gold standard dataset; removing objects below a minimum threshold size from the base image; and retaining the remaining objects if they approximate the line or spine.

A medical system for use in a lithotripsy procedure may include a processor configured to receive input from a first imaging device, wherein the first imaging device may be configured to send image data representative of an image captured in a lumen of a kidney, bladder, or ureter to the processor. The processor may be configured to display the image on a display device coupled to the processor, and analyze the image to sense the presence of an object within the image. If an object was sensed within the image, the processor may analyze the image to estimate a size of the object, and display the estimate on the display device.

An endoscopic system includes an endoscopic imager configured to capture image frames of a target site within a living body and a processor configured to apply a spatial transform to a preliminary set of image frames, the spatial transform converting the image frames into cylindrical coordinates; calculate a map image from the spatially transformed image frames, each pixel position in the map image being defined with a vector of fixed dimension; align a current image frame with the map image and apply the spatial transform to the current image frame; fuse the spatially transformed current image frame to the map image to generate a fused image; and apply an inverse spatial transform to the fused image to generate an enhanced current image frame having a greater spatial resolution than the current image frame. The system also includes a display displaying the enhanced current image frame.

Using computer-assisted classification and/or computer-assisted segmentation with or without monitoring the field of view for change, the workflow for ultrasound imaging may be made more efficient. The classification and/or segmentation is used to perform a next act in the sequence of acts making up the ultrasound examination. Rather than requiring a user to determine the act and implement the act, the ultrasound scanner determines and implements the act based on the identification and/or location of an imaged object. For example, the identification of the object as a kidney using a machine-learnt classifier triggers a color flow scan, and the location of the object determines a placement for the color flow region of interest (ROI), avoiding the user having to perform the ROI initiation and/or placement and increasing workflow efficiency.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.