At the time of automatic selection of a key image from among a large number of medical examination images, a medical imaging apparatus compares information extracted based on a sample image created in advance with information of an examination image, and performs common category determination and similarity degree determination. As a result of the comparison, the examination image selected to be close to the sample image is set as the key image, and storage processing is performed. The stored key image can be used for medical reports.

An endoscopic image viewing support server includes an endoscopic image acquiring unit configured to acquire imaging information, a section-of-interest setting unit configured to estimate a degree of interest and classify a plurality of endoscopic images in accordance with the degree of interest, and an endoscopic image selecting unit configured to select an endoscopic image from each of sections of interest at a ratio based on the degree of interest. The section-of-interest setting unit is configured to determine an endoscopic image of interest from among the plurality of endoscopic images, and in a case where the plurality of endoscopic images are arranged in a chronological order, estimate the degree of interest for an endoscopic image in an endoscopic image group including the endoscopic image of interest by using image processing.

An imaging control device includes: a region dividing unit that divides a taken image into a plurality of regions; a luminance value calculating unit that calculates the average luminance value of each region obtained by division by the region dividing unit; a region identifying unit that identifies a region in which the average luminance value calculated by the luminance value calculating unit is equal to or greater than a predetermined threshold value; a photometric range deciding unit; and a signal processing unit.

A portable endoscopic system comprising an imaging unit for an endoscopic procedure. The imaging unit has an imaging coupler for receiving imaging information from an imaging assembly of an endoscope; a display integrated into a housing of the imaging unit; an image processing unit for processing the received imaging information into images of a time series and to displaying the image in real-time; a motion sensor configured to detect a motion of the housing; and a detection processing unit. The detection processing unit is configured to classify at least one anatomical feature in each image of the time series based on an artificial intelligence classifier; determine a confidence metric of the classification; determine a motion vector based on the detected motion; and display, concurrently with the corresponding image, the classification of the at least one anatomical feature, the determined confidence metric, and the determined motion vector.

An imaging device for producing images of a scene, the imaging device comprising: a first and a second hyperchromatic lens being arranged in a stereoscopic configuration to receive light from the scene; image sensor circuitry configured to capture a first and second image of the light encountered by the first and the second lens respectively; processor circuitry configured to: produce depth information using the captured first and second images of the scene and produce a resultant first and second image of the scene using both the captured first and second image and the depth information.

An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Various embodiments of the present disclosure encompass manipulative endoscopic guidance device employing an endoscopic viewing controller (20) for controlling a display of an endoscopic view (11) of an anatomical structure, and a manipulative guidance controller (30) for controlling a display of one or more guided manipulation anchors (50-52) within the display of the endoscopic view (11) of the anatomical structure. A guided manipulation anchor (50-52) is representative of a location marking and/or a motion directive of a guided manipulation of the anatomical structure (e.g., grasping, pulling, pushing, sliding, reorienting, tilting, removing, or repositioning of the anatomical structure). The manipulative guidance controller (30) may generate an anchor by analyzing a correlation of the endoscopic view (11) of the anatomical structure with a knowledge base of image(s), model(s) and/or details corresponding to the anatomical structure and by deriving the anchor based on a degree of correlation of the endoscopic view (11) of the anatomical structure with the knowledge base.

There is provided a medical image processing apparatus including: an association processing section configured to associate multiple medical captured images in which an observation target is imaged by each of multiple imaging devices including imaging devices in which one or both of an in-focus position and an in-focus range are different; and a compositing processing section configured to depth-composite each of a medical captured image for a right eye and a medical captured image for a left eye among the multiple medical captured images by using an associated other medical captured image.

A computer-implemented system is provided that receives a real-time video captured from a medical image device during a medical procedure. The real-time video may include a plurality of frames. The system may be adapted to detect an object of interest in the plurality of frames and apply one or more neural networks configured to identify a plurality of characteristics of the detected object of interest, such as classification, size, and/or location. In some embodiments, the system is adapted to identify, based on one or more of the plurality of characteristics, a medical guideline and present, in real-time on a display device during the medical procedure, information for the medical guideline.

A capsule endoscope device, system and method for dynamically adjusting a color illumination spectrum. A plurality of different color groups of LEDs may emit light, recorded by an image sensor, corresponding to a plurality of different respective wavelength subranges. A driving circuit may send a driving current to independently activate each different color group of LEDs during entirely or partially non-overlapping time pulses. A single color group of LEDs is independently activated at any one time, and the plurality of different color groups of LEDs are sequentially activated in successive time pulses to simulate a white light or multi-color illumination spectrum over a plurality of the time pulses. The activation pattern of the color groups of LEDs may be dynamically adjusted, in real-time, to achieve a flexible and customizable illumination spectrum ideal for imaging a variety of different environments e.g., in the GI tract.