A system and methods for polyp detection using optical colonoscopy images are provided. In some aspects, the system includes an input configured to receive a series of optical images, and a processor configured to process the series of optical images with steps comprising of receiving an optical image from the input, constructing an edge map corresponding to the optical image, the edge map comprising a plurality of edge pixel, and generating a refined edge map by applying a classification scheme based on patterns of intensity variation to the plurality of edge pixels in the edge map. The processor may also process the series with steps of identifying polyp candidates using the refined edge map, computing probabilities that identified polyp candidates are polyps, and generating a report, using the computed probabilities, indicating detected polyps. The system also includes an output for displaying the report.

A system and methods for detecting polyps using optical images acquired during a colonoscopy. In some aspects, a method includes receiving the set of optical images from the input and generating polyp candidates by analyzing the received set of optical images. The method also includes generating a plurality of image patches around locations associated with each polyp candidate, applying a set of convolutional neural networks to the corresponding image patches, and computing probabilities indicative of a maximum response for each convolutional neural network. The method further includes identifying polyps using the computed probabilities for each polyp candidate, and generating a report indicating identified polyps.

Mechanisms for simultaneously monitoring colonoscopic video quality and detecting polyps in colonoscopy are provided. In some embodiments, the mechanisms can include a quality monitoring system that uses a first trained classifier to monitor image frames from a colonoscopic video to determine which image frames are informative frames and which image frames are non-informative frames. The informative image frames can be passed to an automatic polyp detection system that uses a second trained classifier to localize and identify whether a polyp or any other suitable object is present in one or more of the informative image frames.

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.

An endoscope system includes an endoscope and an image processing device attached to one another. The image processing device includes at least one processor configured to perform operations of determining an operator's action based on an action signal from an endoscope inserted into a subject body, deciding whether an image is set as a detection target image based on the operator's action and detecting a specific region from the detection target image. The processor performs an operation of determining whether the operator's action at a time of capturing the image is a treatment action to give the subject body a treatment. Furthermore, the processor detects, from the image, a region, which exhibits a specular reflection and whose time change in area and position is large, as a washed region and then determine the operator's action at the time of capturing the image is the treatment action when the washed region is detected.

The image processing device 1X includes an acquisition means 30X, a mask image generation means 33X, and a selection means 34X. The acquisition means 30X acquires time series images obtained by photographing an examination target by a photographing unit provided in an endoscope. The mask image generation means 33X generates a plurality of mask images, which indicate candidate regions for an attention part with different levels of granularity, for each of the time series images. Then, the selection means 34X selects an output image for output use from the time series images, based on the plurality of mask images.

A system and methods for detecting polyps using optical images acquired during a colonoscopy. In some aspects, a method includes receiving the set of optical images from the input and generating polyp candidates by analyzing the received set of optical images. The method also includes generating a plurality of image patches around locations associated with each polyp candidate, applying a set of convolutional neural networks to the corresponding image patches, and computing probabilities indicative of a maximum response for each convolutional neural network. The method further includes identifying polyps using the computed probabilities for each polyp candidate, and generating a report indicating identified polyps.

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.

A generic unified deep model for learning from multiple tasks, in the context of medical image analysis includes means for receiving a training dataset of medical images; training the AI model to generate a trained AI model using a pre-processing operation, a Swin Transformer-based segmentation operation, and a post-processing operation, in which application of a Non-Maximum Suppression (NMS) algorithm generates object detection and classification output parameters for the AI model by removing overlapping detections and selecting a best set of detections according to a determined confidence score for the detections remaining; and outputting the trained AI model for use with medical image analysis.

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.