Data Efficiency of Segment Anything Model for Optic Disc and Cup Segmentation

We investigated the performance of Segment Anything Model (SAM)—the first promptable foundation model for image segmentation—for optic disc (OD) and optic cup (OC) segmentation when fine-tuned on progressively smaller number of fundus images. Three different implementations of SAM with an input prompt were considered: (1) SAM with an OD/OC-centred bounding box (SAM GT); (2) SAM with a noise-added (e.g. displacement, size variation) bounding box (SAM Noise); and (3) SAM with an automatically predicted (using Faster R-CNN) bounding box (SAM Auto). Two popular pre-trained semantic segmentation models, DeepLabV3 with a MobileNetV3-Large backbone and DeepLabV3 with a ResNet-50 backbone were used as baseline models. For OD segmentation, ResNet-50 exhibited comparable if not higher data efficiency (i.e. good performance despite limited training data) than even the most optimal implementation of SAM (SAM GT), although SAM was evidently more robust to small training set sizes, e.g. 25, than MobileNetV3-Large and in eyes with more challenging OD morphologies, e.g. significant peri-papillary atrophy. For OC segmentation, however, SAM GT and SAM Noise consistently demonstrated higher data efficiency, particularly in eyes with relatively small cup-to-disc ratio and ill-defined OC margin

A publicly available vessel segmentation algorithm for SLO images

Background and Objective: Infra-red scanning laser ophthalmoscope (IRSLO) images are akin to colour fundus photographs in displaying the posterior pole and retinal vasculature fine detail. While there are many trained networks readily available for retinal vessel segmentation in colour fundus photographs, none cater to IRSLO images. Accordingly, we aimed to develop (and release as open source) a vessel segmentation algorithm tailored specifically to IRSLO images. Materials and Methods: We used 23 expertly annotated IRSLO images from the RAVIR dataset, combined with 7 additional images annotated in-house. We trained a U-Net (convolutional neural network) to label pixels as 'vessel' or 'background'. Results: On an unseen test set (4 images), our model achieved an AUC of 0.981, and an AUPRC of 0.815. Upon thresholding, it achieved a sensitivity of 0.844, a specificity of 0.983, and an F1 score of 0.857. Conclusion: We have made our automatic segmentation algorithm publicly available and easy to use. Researchers can use the generated vessel maps to compute metrics such as fractal dimension and vessel density.Comment: 9 pages, 4 figure

Elevational Spatial Compounding for enhancing image quality in Echocardiography

INTRODUCTION: Echocardiography is commonly used in clinical practice for the real-time assessment of cardiac morphology and function. Nevertheless, due to the nature of the data acquisition, cardiac ultrasound images are often corrupted by a range of acoustic artefacts, including acoustic noise, speckle and shadowing. Spatial compounding techniques have long been recognised for their ability to suppress common ultrasound artefacts, enhancing the imaged cardiac structures. However, they require extended acquisition times as well as accurate spatio-temporal alignment of the compounded data. Elevational spatial compounding acquires and compounds adjacent partially decorrelated planes of the same cardiac structure. METHODS: This paper employs an anthropomorphic left ventricle phantom to examine the effect of acquisition parameters, such as inter-slice angular displacement and 3D sector angular range, on the elevational spatial compounding of cardiac ultrasound data. RESULTS AND CONCLUSION: Elevational spatial compounding can produce substantial noise and speckle suppression as well as visual enhancement of tissue structures even for small acquisition sector widths (2.5° to 6.5°). In addition, elevational spatial compounding eliminates the need for extended acquisition times as well as the need for temporal alignment of the compounded datasets. However, moderate spatial registration may still be required to reduce any tissue/chamber blurring side effects that may be introduced

Corneal biomechanics are not exclusively compromised in high myopia

IntroductionResearch assuming linearity has concluded that corneal biomechanics are compromised in high myopia. We investigated whether this assumption was appropriate and re-examined these associations across different levels of myopia.MethodsMyopic (spherical equivalent refraction, SER ≤ −0.50 D) eyes of 10,488 adults aged 40–69 years without any history of systemic and ocular conditions were identified in the UK Biobank. Ordinary least squares (OLS) regression was employed to test the linear association between corneal hysteresis (CH) or corneal resistance factor (CRF), separately, and SER while controlling for age, sex, corneal radius and intraocular pressure. Quantile regression (QR) was used to test the same set of associations across 49 equally spaced conditional quantiles of SER.ResultsIn OLS regression, each standard deviation (SD) decrease in CH and CRF was associated with 0.08 D (95% CI: 0.04–0.12; p < 0.001) and 0.10 D (95% CI: 0.04–0.15; p < 0.001) higher myopia, respectively. However, residual analysis indicated that the linearity assumption was violated. QR revealed no evidence of a significant association between CH/CRF and SER in low myopia, but a significant (p < 0.05) positive association became evident from −2.78 D (0.06 and 0.08 D higher myopia per SD decrease in CH and CRF). The magnitude of association increased exponentially with increasing myopia: in the −5.03 D quantile, every SD decrease in CH and CRF was associated with 0.17 D (95% CI: 0.08–0.25; p < 0.001) and 0.21 D (95% CI: 0.10–0.31; p < 0.001) higher myopia. In the −8.63 D quantile, this further increased to 0.54 D (95% CI: 0.33–0.76; p < 0.001) and 0.67 D (95% CI: 0.41–0.93; p < 0.001) higher myopia per SD decrease in CH and CRF.ConclusionsCorneal biomechanics appeared compromised from around −3.00 D. These changes were observed to be exponential with increasing myopia

Multi-modal retinal scanning to measure retinal thickness and peripheral blood vessels in multiple sclerosis

Our purpose was to investigate changes to the retina in multiple sclerosis (MS) using established and novel modes of retinal image acquisition and analysis. 72 participants with MS and 80 healthy volunteers underwent retinal scanning with optical coherence tomography (OCT) and ultra-widefield (UWF) scanning laser ophthalmoscopy (SLO), over a two-year period. Changes in retinal nerve fibre layer (RNFL) thickness, macular volume and retinal blood vessel diameter were measured and parameters were then tested for associations with MS. Measurements from OCT showed that individuals with MS had a thinner RNFL and reduced macular volume when compared to healthy volunteers. On UWF images, participants with MS had reduced arterial widths in the inferior nasal quadrant of both eyes and reduced venous widths in the inferior nasal quadrant of right eyes. Longitudinal analysis showed that participants with MS had an accelerated annual rate of RNFL thinning in several regions of the retina. In conclusion, the assessment of OCT showed thinning of the RNFL and macula in concordance with previous reports on MS, while analysis of blood vessels in the retinal periphery from UWF-SLO images revealed novel changes