in this work we evaluated a postprocessing, customized automatic retinal oct B-scan enhancement software for noise reduction, contrast enhancement and improved depth quality applicable to Heidelberg engineering Spectralis oct devices. A trained deep neural network was used to process images from an oct dataset with ground truth biomarker gradings. performance was assessed by the evaluation of two expert graders who evaluated image quality for B-scan with a clear preference for enhanced over original images. objective measures such as SnR and noise estimation showed a significant improvement in quality. Presence grading of seven biomarkers IRF, SRF, ERM, Drusen, RPD, GA and iRoRA resulted in similar intergrader agreement. intergrader agreement was also compared with improvement in iRf and RpD, and disagreement in high variance biomarkers such as GA and iRoRA. OCT is a non-invasive, micrometer-resolution imaging technique that has found wide application in the diagnosis of corneal and retinal pathologies. Thanks to advances in electronics, precision optics and signal processing, OCT technology has steadily improved in image quality, speed and resolution. However, speckle noise and signal loss in deeper tissue remains a major limitation. Speckle noise is caused by a complex combination of thermal, electrical, multiple-scattering effects, as well as digital processing algorithms. Indeed, in retinal imaging, it is common to consider up to 75% of the pixel values as noise 1,2. A common approach to improving OCT image quality is to acquire and average multiple scans of the same location. Assuming that noise is uncorrelated between the acquired images, the average of N images will improve the signal-to-noise by a factor of N while correlated noise will reduce the improvement in practice. Consequently, the approach requires a longer acquisition time, by a factor of N, during which the patient is required to fixate motionless on a fixation target. While this approach helps to improve images of patients with clear media, it results in rather unsatisfactory results in patients with media opacities e.g. cataracts. To mitigate this, commercial OCT devices often include a separate optical eye tracking system to support the process, with corresponding increases in cost and device complexity. Imperfections in patient fixation and the eye tracking system lead to blurriness in the averaged scans. Combined, the above create a practical ceiling to the image quality improvement that can be extracted from image averaging. See Fig. 1 for denoising and averaging examples. Traditionally, digital noise removal attempts to post-process acquired images to reduce the amount of speckle noise without harming the structural information presence in the images sample. We identify two main areas in which OCT denoising has been evaluated, the first one considers spatial denoising methods, where image enhancement happens either via local image filtering such as median 3 or mean Gaussian filters 4 , or at global OCT volume scale. The latter includes B...
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