Abstract: An analysis apparatus analyses a video image signal comprising successive frames of imaging an endoscopy procedure. A machine learning block analyses the video image signal using a machine learning technique that classifies regions of the frames as belonging to one of plural classes corresponding to respective types of image artefact. The classes include a motion blur class corresponding to motion blur of the image, at least one erroneous exposure class corresponding to a type of erroneous exposure of the image, and at least one noise artefact class corresponding to a type of image artefact that is noise. A quality score block derives quality scores representing image quality of the successive frames based on the classified regions.

Abstract: This disclosure teaches multi-classification of endoscopic imaging using a segmentation neural network. The network is trained on imaging data using a novel loss function with components of both focal and boundary loss. During a lithotripsy procedure, images are received by a deployed endoscopic probe. Based on the received imaging data and inference data from its prior learning, the segmentation neural network identifies renal calculi and surgical instruments within the imaging data. The imaging data is modified and displayed to assist the lithotripsy procedure.

Abstract: This disclosure teaches determining a renal calculus size based on calibration data generated from an endoscopic imager. During a lithotripsy procedure, images are received by a deployed endoscopic probe. Based on the received imaging data, the generated calibration data, and known properties of the endoscopic probe, one or more renal calculi in the images are identified and sized. The determined size values are displayed with the images of the renal calculi.

Abstract: An area of Barrett's oesophagus in a subject's oesophagus is quantified from a video image signal representing a video image of the subject's oesophagus captured using a camera of an endoscope. Depth estimation on the frames to derive depth maps in respect of frames of the video image. Regions of the frames corresponding to an area of Barrett's oesophagus in the subject's oesophagus are segmented. A value of a geometrical measure of the area of Barrett's oesophagus is calculated using the depth map and segmented region in respect of at least one of the frames.

Abstract: An analysis apparatus analyses a video image signal comprising successive frames of imaging an endoscopy procedure. A machine learning block analyses the video image signal using a machine learning technique that classifies regions of the frames as belonging to one of plural classes corresponding to respective types of image artefact. The classes include a motion blur class corresponding to motion blur of the image, at least one erroneous exposure class corresponding to a type of erroneous exposure of the image, and at least one noise artefact class corresponding to a type of image artefact that is noise. A quality score block derives quality scores representing image quality of the successive frames based on the classified regions.