An image processing apparatus includes a hardware processor. The hardware processor calculates a characteristic amount from each frame images of a moving image. The characteristic amount indicates change in a subject. The moving image is obtained by radiographing a dynamic state of the subject. The hardware processor extracts a frame image having the characteristic amount satisfying a predetermined condition, processes the extracted frame image by applying an image processing parameter to the extracted frame image prior to a remaining frame image, and causes a display to display the processed frame image.

A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastritis, in a subject are disclosed. The diagnostic system includes a processor that obtains an image of a stomach of the subject collected by an endoscope. The processor can compare the subject image with reference normal and abnormal stomach images. If the subject image is abnormal, the processor can generate a subject abnormality image indicative of the differences between the subject image and the normal image. By comparing the generated subject abnormality image with reference abnormality images representative of different severity levels of gastritis, the processor can diagnose the subject as having a particular severity level of gastritis.

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network (CNN) trains the CNN using a first endoscopic image of the digestive organ and at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, or information corresponding to a severity level, the final diagnosis result being corresponding to the first endoscopic image, and the trained CNN outputs at least one of a probability of the positivity and/or the negativity for the disease in the digestive organ, a severity level of the disease, or a probability corresponding to the invasion depth (infiltration depth) of the disease, based on a second endoscopic image of the digestive organ.

Disclosed herein are computer-implemented method, system, and computer-program product (computer-readable storage medium) embodiments of image scoring for intestinal pathology. An embodiment includes receiving, via at least one processor, an output of an imaging device. The output of the imaging device may include a plurality of image frames forming at least a subset of a set of image frames depicting an inside surface of a digestive organ of a given patient; and decomposing, via at least one machine learning (ML) algorithm, at least one image frame of the plurality of image frames into a plurality of regions of interest. The at least one region of interest may be defined by determining that an edge value exceeds a predetermined threshold. At least one processor may automatically assign a first score based at least in part on the edge value for each region of interest and automatically shuffle the set of image frames.

A computer-implemented method, comprising applying training images of an environment divided into zones to a neural network, and performing classification to label a test image based on a closest zone of the zones; extracting a feature from retrieved training images and pose information of the test image that match the closest zone; performing bundle adjustment on the extracted feature by triangulating map points for the closest zone to generate a reprojection error, and minimizing the reprojection error to determine an optimal pose of the test image; and for the optimal pose, providing an output indicative of a location or probability of a location of the test image at the optimal pose within the environment.

A visualization system including a video processing apparatus (VPA) including a processor; and memory having processing instructions stored therein and executable by the processor, the processing instructions operable to, when executed by the processor: determine an amplification gain level applied by the image sensor; determine, based on the amplification gain level, a denoising level and a corresponding sharpening level; and process image data to denoise and sharpen an image corresponding to the image signals using the denosing level and the sharpening level.

To provide an endoscope processor (2) and the like that can output the aggregation results of lesions for each examination part. The endoscope processor (2) according to one aspect includes an image acquisition unit that acquires a captured image from a large-intestine endoscope (1), a part identification unit that identifies a part in a large intestine based on the captured image acquired by the image acquisition unit, a polyp extraction unit that extracts a polyp from the captured image, an aggregation unit that aggregates the number of polyps for each part identified by the part identification unit, and an output unit that outputs an endoscope image based on the captured image and an aggregation result aggregated by the aggregation unit.

There is provided a computer implemented method for detection of likelihood of malignancy in an anatomical image of a patient for treatment planning, comprising: receiving an anatomical image, feeding the anatomical image into a global component of a model trained to output a global classification label, feeding the anatomical image into a local component of the model trained to output a localized boundary, feeding the anatomical image patch-wise into a patch component of the model trained to output a patch level classification label, extracting a respective set of regions of interest (ROIs) from each one of the components, each ROI indicative of a region of the anatomical image likely to include an indication of malignancy, aggregating the ROIs from each one of the components into an aggregated set of ROIs, and feeding the aggregated set of ROIs into an output component that outputs an indication of likelihood of malignancy.

A method and a system for detecting and analyzing a mucosa of a digestive tract are provided. The method includes detecting reply signals from the mucosa of the digestive tract within a depth range, acquiring 2D vascular images by performing a vascular enhancement on the reply signals, constructing a 3D vascular contrasting image of at least part of the mucosa of the digestive tract within the depth range by recombining at least part of the 2D vascular images, and reconstructing a 3D vascular contrasting projection image by performing a projection process to the 3D vascular contrasting image, and defining a stage of the mucosa of the digestive tract within the depth range according to the 3D vascular contrasting projection image, the 3D vascular contrasting image, the 2D vascular images, and vessel morphologies shown therein.

Devices, systems, and methods are provided for recognizing, diagnosing, mapping, sensing, monitoring and/or treating selected areas within a patient's body. The systems, devices and methods may be used to map, detect and/or quantify images and/or physiological parameters collected from the patient. One such system comprises an optical imaging device, such as an endoscope, and a processor coupled to the imaging device. The processor includes a software application configured to recognize the images captured by the optical imaging device and determine if the tissue contains a medical condition and may include an artificial neural network configured to develop at least one set of computer-executable rules useable to recognize the medical condition in the captured tissue images. The systems, devices and methods provided herein allow for a more objective and comprehensive inspection of the targeted areas within a patient so as to improve the diagnosis and ultimate treatment of patients.