A support device for an endoscope comprises a tubular member configured for removable attachment to an outer surface of the endoscope near, or at, its distal end and a plurality of projecting elements extending outward from the outer surface of the tubular member and circumferentially spaced from each other. The device includes an optically transparent cover coupled to the tubular member and configured for covering the distal end of the endoscope when the tubular member is attached to the outer surface of the endoscope. The projecting elements provide support for the endoscope, improve visualization and center the scope as it passes through a body lumen, such as the colon. In addition, the cover seals the distal end of the endoscope to protect the scope and its components from debris, fluid, pathogens and other biomatter.

A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentation results.

An image processing apparatus includes a processor including hardware. The processor is configured to: set a first acquisition condition and a second acquisition condition for a video processor configured to acquire a first image that is based on a display-purpose acquisition condition for acquiring an image for display at a frame rate for viewing and a second image that is based on an analysis-purpose acquisition condition for acquiring an image for image analysis at a frame rate that is lower than the frame rate for viewing, in a mixed manner, the first acquisition condition including the display-purpose acquisition condition, the second acquisition condition including the display-purpose acquisition condition and the analysis-purpose acquisition condition; generate support information based on an image analysis result; and control switching between the first acquisition condition and the second acquisition condition according to the image analysis result.

Provided are computer-implemented methods and systems for generating and/or utilizing data analysis algorithm(s) for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

The present disclosure provides systems and methods for hybrid imaging. The systems and methods may obtain a first magnetic resonance (MR) image of a target object. The first MR image may be acquired by a magnetic resonance imaging (MRI) device using a first imaging sequence. The systems and methods may also obtain a second MR image of the target object. The second MR image may be acquired by the MRI device using a second imaging sequence. The second MR image may correspond to a target respiratory phase of the target object. The systems and methods may also obtain a target emission computed tomography ECT) image of the target object. The target ECT image may correspond to the target respiratory phase. The systems and methods may further fuse, based on the second MR image, the first MR image and the target ECT image.

There is provided a method, comprising: feeding 2D image(s) of an internal surface of a colon captured by an endoscopic camera, into a machine learning model, wherein the 2D image(s) excludes a depiction of an external measurement tool, wherein the machine learning model is trained on records, each including 2D images of the internal surface of the colon of a respective subject labelled with ground truth labels of respective bounding boxes enclosing respective polyps and at least one of an indication of size and a type of the respective polyp indicating likelihood of developing maligiancy, obtaining a bounding box for a polyp and at least one of an indication of size and type of the polyp, and generating instructions for presenting within the GUI, an overlay of the bounding box over the polyp and the at least one of the indication of size and type of the polyp.

A method, a controller, and a surgical hybrid navigation system for transferring a registration of three dimensional image data of a surgical object from a first to a second surgical navigation system are described. A first tracker that is detectable by a first detector of the first surgical navigation system is arranged in a fixed spatial relationship with the surgical object and a second tracker that is detectable by a second detector of the second surgical navigation system is arranged in a fixed spatial relationship with the surgical object. The method includes registering the three dimensional image data of the surgical object in a first coordinate system of the first surgical navigation system and determining a first position and orientation of the first tracker in the first coordinate system and a second position and orientation of the second tracker in a second coordinate system of the second surgical navigation system.

A colon cleanliness indicating device including a forward toilet-secured receiving section configured with a recessed receptacle for receiving colonic effluent and a rearward indicating section for indicating a degree of colon cleanliness. The indicating section is formed with one or more channels in fluid communication with the receptacle through which the received colonic effluent is flowable and by which the received colonic effluent can be visualized in order to assess a degree of colon cleanliness.