Abstract: An apparatus for generating an alert indicating an unreliability in a location of a landmark feature in a retinal image predicted by a machine learning algorithm, comprising: a receiver module which receives the predicted location; a probability indication determining module which uses the predicted location and a mixture model, which comprises a probability distribution of a landmark feature location and is based on determined locations of the landmark feature in retinal images having a plurality of retinal image classes, to determine, for each class, a respective probability indication indicating a probability that the retinal image belongs to the class; an outlier detector module which uses the probability indications to determine whether the retinal image is an outlier belonging to none of the classes; and an alert generator module which generates, when the retinal image belongs to none of the classes, an alert indicating unreliability of the predicted location.

Abstract: An apparatus for generating an alert indicating an unreliability in a location of a landmark feature in a retinal image predicted by a machine learning algorithm, comprising: a receiver module which receives the predicted location; a probability indication determining module which uses the predicted location and a mixture model, which comprises a probability distribution of a landmark feature location and is based on determined locations of the landmark feature in retinal images having a plurality of retinal image classes, to determine, for each class, a respective probability indication indicating a probability that the retinal image belongs to the class; an outlier detector module which uses the probability indications to determine whether the retinal image is an outlier belonging to none of the classes; and an alert generator module which generates, when the retinal image belongs to none of the classes, an alert indicating unreliability of the predicted location.

Abstract: A method of method of processing optical coherence tomography, OCT, image data representing an OCT image of a retina of an eye, to generate mapping data which maps out a predetermined band of a plurality of distinct bands which extend across the OCT image and correspond to respective anatomical layers of the retina. The method comprises: receiving the OCT image data; processing A-scan data of the received OCT image data to generate data indicative of sequences of A-scan elements corresponding to the predetermined band of the plurality of distinct bands and having respective A-scan element values that vary in accordance with a predetermined pattern; and generating the mapping data by applying a line-finding algorithm to determine a line passing through the sequences of A-scan elements indicated by the generated data.

Abstract: An ocular imaging system comprising: an image acquisition module which acquires a retinal image; a landmark location prediction module which predicts locations of at least two landmarks in the retinal image; and an apparatus for alerting a user to an unreliability in at least one of the predicted locations. The apparatus receives the predicted first locations; uses the predicted locations to evaluate a distance metric indicative of a distance between the landmark features; use data indicative of a probability distribution of a distance between the landmark features obtained from measurements of the distance in retinal images different from the retinal image to determine an indication of whether the evaluated distance metric lies outside a predetermined interval about a peak the probability distribution; and generates an alert indicating the unreliability when the determined indication indicates that the evaluated distance metric lies outside the predetermined interval.

Abstract: A method, apparatus, and computer-readable medium, for assessing image quality of an image produced by a scanning imaging system. The method comprises acquiring (S10) image data of an image produced by the scanning imaging system and calculating (S20 to S40), for each section of the image: a respective first value measuring at least one of sharpness or contrast of at least a part of the section, the measuring depending on noise, a respective second value measuring noise in at least a part of the section, and a respective third value indicating image quality, by combining the first and second values. The combining is such that calculated third values have a weaker dependency on the noise than the first values. The method further comprises determining (S50) a quality score that is indicative of image quality of the image based on a variation of the calculated third values among the sections.

Abstract: A method of method of processing optical coherence tomography, OCT, image data representing an OCT image of a retina of an eye, to generate mapping data which maps out a predetermined band of a plurality of distinct bands which extend across the OCT image and correspond to respective anatomical layers of the retina. The method comprises: receiving the OCT image data; processing A-scan data of the received OCT image data to generate data indicative of sequences of A-scan elements corresponding to the predetermined band of the plurality of distinct bands and having respective A-scan element values that vary in accordance with a predetermined pattern; and generating the mapping data by applying a line-finding algorithm to determine a line passing through the sequences of A-scan elements indicated by the generated data.

Abstract: A method, apparatus, and computer-readable medium, for assessing image quality of an image produced by a scanning imaging system. The method comprises acquiring (S10) image data of an image produced by the scanning imaging system and calculating (S20 to S40), for each section of the image: a respective first value measuring at least one of sharpness or contrast of at least a part of the section, the measuring depending on noise, a respective second value measuring noise in at least a part of the section, and a respective third value indicating image quality, by combining the first and second values. The combining is such that calculated third values have a weaker dependency on the noise than the first values. The method further comprises determining (S50) a quality score that is indicative of image quality of the image based on a variation of the calculated third values among the sections.