Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development

All the colours of the rainbow.

Our perception of colour has always been a source of fascination, so it's little wonder that studies of the phenomenon date back hundreds of years. What, though, can modern scientists learn from medieval literature — and how do we go about it

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance

How do (perceptual) distracters distract?

When a target stimulus occurs in the presence of distracters, decisions are less accurate. But how exactly do distracters affect choices? Here, we explored this question using measurement of human behaviour, psychophysical reverse correlation and computational modelling. We contrasted two models: one in which targets and distracters had independent influence on choices (independent model) and one in which distracters modulated choices in a way that depended on their similarity to the target (interaction model). Across three experiments, participants were asked to make fine orientation judgments about the tilt of a target grating presented adjacent to an irrelevant distracter. We found strong evidence for the interaction model, in that decisions were more sensitive when target and distracter were consistent relative to when they were inconsistent. This consistency bias occurred in the frame of reference of the decision, that is, it operated on decision values rather than on sensory signals, and surprisingly, it was independent of spatial attention. A normalization framework, where target features are normalized by the expectation and variability of the local context, successfully captures the observed pattern of results

How do (perceptual) distracters distract?

When a target stimulus occurs in the presence of distracters, decisions are less accurate. But how exactly do distracters affect choices? Here, we explored this question using measurement of human behaviour, psychophysical reverse correlation and computational modelling. We contrasted two models: one in which targets and distracters had independent influence on choices (independent model) and one in which distracters modulated choices in a way that depended on their similarity to the target (interaction model). Across three experiments, participants were asked to make fine orientation judgments about the tilt of a target grating presented adjacent to an irrelevant distracter. We found strong evidence for the interaction model, in that decisions were more sensitive when target and distracter were consistent relative to when they were inconsistent. This consistency bias occurred in the frame of reference of the decision, that is, it operated on decision values rather than on sensory signals, and surprisingly, it was independent of spatial attention. A normalization framework, where target features are normalized by the expectation and variability of the local context, successfully captures the observed pattern of results

Bright-light distractions and visual performance

Visual distractions pose a significant risk to transportation safety, with laser attacks against aircraft pilots being a common example. This study used a research-grade High Dynamic Range (HDR) display to produce bright-light distractions for 12 volunteer participants performing a combined visual task across central and peripheral visual fields. The visual scene had an average luminance of 10 cd∙m−2 with targets of approximately 0.5° angular size, while the distractions had a maximum luminance of 9,000 cd∙m−2 and were 3.6° in size. The dependent variables were the mean fixation duration during task execution (representative of information processing time), and the critical stimulus duration required to support a target level of performance (representative of task efficiency). The experiment found a statistically significant increase in mean fixation duration, rising from 192 ms without distractions to 205 ms with bright-light distractions (p = 0.023). This indicates a decrease in visibility of the low contrast targets or an increase in cognitive workload that required greater processing time for each fixation in the presence of the bright-light distractions. Mean critical stimulus duration was not significantly affected by the distraction conditions used in this study. Future experiments are suggested to replicate driving and/or piloting tasks and employ bright-light distractions based on real-world data, and we advocate the use of eye-tracking metrics as sensitive measures of changes in performance

All the colours of the rainbow

Our perception of colour has always been a source of fascination, so it's little wonder that studies of the phenomenon date back hundreds of years. What, though, can modern scientists learn from medieval literature — and how do we go about it

Compact, modular and in-plane AOSLO for high-resolution retinal imaging

The adaptive optics scanning laser ophthalmoscope (AOSLO) was first developed in 2002 and since then the technology has been adopted in several laboratories around the world, for both clinical and psychophysical research. There have been a few major design implementations of the AOSLO. The first used on-axis tilted spherical mirrors in a planar arrangement, and the second minimized the build up of astigmatism present in the first design by using a non-planar arrangement. Other designs have avoided astigmatism by using custom-made toroidal mirrors or by using lenses on-axis, rather than mirrors. We present a new design implementation for an AOSLO that maintains a planar optical alignment without the build up astigmatism using compact, reconfigurable modules based on an Offner relay system. We additionally use an off-the-shelf digital oscilloscope for data capture and custom-written Python code for generating and analyzing the retinal images. This design results in a compact system that is simple to align and, being composed of modular relays, has the potential for additional components to be added. We show that this system maintains diffraction-limited image quality across the field of view and that cones are clearly resolved in the central retina. The modular relay design is generally applicable to any system requiring one or more components in the pupil conjugate plane. This is likely to be useful for any point-scanned system, such as a standard scanning laser ophthalmoscope or non-ophthalmic confocal imaging system