An endoscope system capable of setting the optimal balance of light source wavelengths in accordance with a diagnosis purpose is provided. An endoscope system includes a diagnosis purpose acquisition unit, a plurality of light sources with different light emission wavelengths, a light quantity ratio storage unit, a light quantity ratio selection unit, and a light source control unit. The diagnosis purpose acquisition unit acquires a diagnosis purpose. The light quantity ratio storage unit stores correspondence between the diagnosis purpose and a plurality of light quantity ratios with different balances of respective emission light quantities of the plurality of light sources. The light quantity ratio selection unit refers to the light quantity ratio storage unit and selects the light quantity ratio that is used for the acquired diagnosis purpose. The light source control unit controls the plurality of light sources to emit illumination light with the selected light quantity ratio.

Image recognition may include obtaining a first image, segmenting the first image into a plurality of first regions by using a target model, and searching for a target region among bounding boxes in the first image that use points in the first regions as centers. The target region is a bounding box in the first image in which a target object is located. The target model is a pre-trained neural network model configured to recognize from an image, a region in which the target object is located. The target model is obtained through training by using positive samples with a region in which the target object is located marked and negative samples with a region in which a noise is located marked. The target region is marked in the first image to improve accuracy of target object detection in an image.

An image diagnosis support system includes: an input unit that receives an input of an image; a specifying unit that specifies a specular reflection region and a non-specular reflection region in a region of interest in the image; and a determination unit that determines whether the region of interest is an inadequate region that is inadequate for diagnosis on the basis of an image processing result for at least one of the specular reflection region and the non-specular reflection region.

The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representations of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications to train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.

The present technology relates to an endoscope system in which resolution and an S/N ratio are adjusted to be well-balanced depending on an imaging condition, and further capable of changing a processing load depending on the imaging condition, a method for operating the endoscope system, and a program. From an image signal in a body cavity imaged by an endoscope apparatus, a low frequency image including a low frequency component and a high frequency image including a high frequency component are extracted. The low frequency image is reduced by a predetermined reduction ratio, and, after image quality improvement processing is performed, is enlarged by an enlargement ratio corresponding to the reduction ratio. At that time, based on condition information indicating an imaging state, when brightness at the time of imaging is sufficient and the high frequency component does not include noise components a lot, the low frequency image and the high frequency image are added to be output as an output image. In addition, based on the condition information, when the brightness at the time of imaging is not sufficient and the high frequency component includes the noise components a lot, only the low frequency image is output as the output image. The present technology can be applied to the endoscope system.

Provided are an image processing device, an endoscope system, an image processing method, and a program capable of automatically differentiating a medical image including a scene of interest and supporting saving of the medical image according to a differentiation result.

The image processing device includes a medical image acquisition unit (41) that acquires a medical image, a scene-of-interest recognition unit (51) that recognizes a scene of interest from the medical image acquired using the medical image acquisition unit, a degree-of-similarity calculation unit (52) that, for the scene of interest recognized using the scene-of-interest recognition unit, calculates a degree of similarity between the medical image acquired using the medical image acquisition unit and a standard image determined for the scene of interest, and a saving processing unit (53) that executes processing for saving the medical image in a saving device based on the degree of similarity calculated using the degree-of-similarity calculation unit.

A computer-implemented method for estimating a size of a suspected polyp in an image includes accessing an image of in at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, wherein the image includes a suspected polyp; receiving an indication to an approximated periphery of the suspected polyp in the image; determining, without human intervention, a 3D measurement of the polyp based on peripheral points in the approximated periphery indication; and estimating a size of the suspected polyp based on the determined 3D measurement.

An image processing apparatus including: a memory to store intraluminal images captured by a medical device inserted into a living body, the intraluminal images being associated with time series; and a processor to detect an abnormal area from intraluminal images captured by a medical device inserted into a living body, set a similar abnormal section where the abnormal areas being similar to each other are included, generate information related to the similar abnormal section, extract an intraluminal image that is used as a representative image from among the intraluminal images belonging to the similar abnormal section, control the display device to display the representative image, extract, as the information related to the similar abnormal section from among the intraluminal images, non-representative images which are part of intraluminal images other than the representative image, and control a display device to display the information related to the similar abnormal section.

Provided is a disease diagnosis support method employing endoscopic images of a digestive organ using a neural network, and the like. The disease diagnosis support method employing endoscopic images of a digestive organ using a neural network trains the neural network by using first endoscopic images of the digestive organ, and corresponding to the first endoscopic images, at least one of definitive diagnosis result of being positive or negative for the disease of the digestive organ, a past disease, a severity level, and information corresponding to an imaged region. The trained neural network outputs, based on second endoscopic images of the digestive organ, at least one of a probability of being positive and/or negative for the disease of the digestive organ, a probability of a past disease, a severity level of the disease, and the information corresponding to the imaged region.

In one embodiment, a medical analysis system, includes a display, and processing circuitry to receive a three-dimensional map of an interior surface of a cavity within a body of a living subject, positions on the interior surface being defined in a spherical coordinate system wherein each position is defined by an angular coordinate pair and an associated radial distance from an origin, project the angular coordinate pair of respective positions from the interior surface to respective locations in a two-dimensional plane according to a coordinate transformation, compute respective elevation values from the plane at the respective locations based on at least the radial distance associated with the respective projected angular coordinate pair, and render to the display an image of a partially flattened surface of the interior surface with the partially flattened surface being elevated from the plane according to the computed respective elevation values at the respective locations.