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.

Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.

A medical image processing apparatus includes a port, a processor and a display. The port acquires volume data including a subject. The processor calculates long and short diameters of the subject. The display shows the long and short diameters. A line segment presenting the long diameter and a line segment presenting the short diameter is a pair of skew lines.

The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.

Methods and systems for generating a visualization of a surface of an internal body cavity, such as an internal organ like the bladder, are provided. The approach generally includes inserting an endoscope into an internal body cavity, acquiring a video of the tissue surfaces defining the internal body cavity, stitching video frames together to generate a panoramic map of the tissue surfaces defining the internal body cavity, and displaying the panoramic map.

This application relates to a method of measuring a volume of an organ. In one aspect, the method includes acquiring a plurality of captured images of the organ and photographing metadata and preprocessing the plurality of images to acquire a plurality of image patches of a specified size. The method may also include inputting the plurality of image patches into a three-dimensional (3D) convolutional neural network (CNN)-based neural network model and estimating an organ region corresponding to each of the plurality of image patches. The method may further include measuring a volume of the organ by using an area of the estimated organ region and the photographing metadata. The method may further include measuring an uncertainty value of the 3D CNN-based neural network model and uncertainty values of the plurality of images based on a result of estimating by the 3D CNN-based neural network model.

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.

A system and method of automatically preparing and analyzing urine samples for identifying cancer cells is able to complete conventional diagnostic tasks without lab technicians, cytopathologists, or other medical professionals. The method is provided with at least one source sample, at least one manipulator arm, at least one centrifuge, at least one electronic microscope, and at least one unitary controller. The method is further provided with a cytopathological index containing a visual characteristic database and identification confidence threshold rubrics supporting the automation of visual analyses typically performed manually with a conventional microscope. This method is further provided with a data processing function, wherein data stemming from multiple testing cycles may be collated, formatted, and presented for use by medical professionals in determining and projecting the effectiveness of a course of treatment.

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.

A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.