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The watercolor illusion (WCI) occurs when a physically non-colored region surrounded by contrasting contour and fringe appears filled in with a hue similar to the fringe. The present experiments explored how local and global stimulus factors influence the spatial expanse of WCI color spreading. Experiment 1 utilized two- and three-dimensional-appearing stimuli with the WCI in only one part of each stimulus. Some conditions fully enclosed the color-spreading region with fringe on all sides. Others removed fringe from one side, opening up the color-spreading region to another part of the stimulus. Regardless of perceived dimensionality or enclosure, color did not spread beyond the fringed color-spreading region as confirmed by illusion magnitude ratings and handwritten shading. Experiment 2 consisted of transparent “wireframe” versions of the opaque-appearing stimuli used in Experiment 1. This altered the local context by adding physical contours inside the fringed color-spreading region. As in Experiment 1, color did not spread beyond physically open regions. Furthermore, illusory color filled a space bound by a combination of physical and illusory contours depending on the fringe end-cuts and other perceptual organization cues within the stimulus. Our main focus in these experiments was to determine where color spreads in a variety of contexts. Perceptual organization factors other than perceived depth seem more likely to impact the spatial expanse of WCI color spreading. These are some of the first experiments to explore the impact of changes to local and global context on the spatial expanse of the WCI.

Purpose Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score. Design Retrospective analysis. Participants The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease. Methods A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9. Main Outcome Measures Birth weight, gestational age, postmenstrual age, and vascular severity score. Results Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P Conclusions Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.

Perceptual fading of an artificial scotoma can be viewed as a failure of figure–ground segregation, providing a useful tool for investigating possible mechanisms and processes involved in figure–ground perception. Weisstein’s antagonistic magnocellular/parvocellular stream figure–ground model proposes P stream activity encodes figure, and M stream activity encodes background. Where a boundary separates two regions, the region that is perceived as figure or ground is determined by the outcome of antagonism between M and P activity within each region and across the boundary between them. The region with the relatively stronger P “figure signal” is perceived as figure, and the region with the relatively stronger M “ground signal” is perceived as ground. From this perspective, fading occurs when the figure signal is overwhelmed by the ground signal. Strengthening the figure signal or weakening the ground signal should make the figure more resistant to fading. Based on research showing that red light suppresses M activity and short wavelength sensitive S-cones provide minimal input to M cells, we used red and blue light to reduce M activity in both figure and ground. The time to fade from stimulus onset until the figure completely disappeared was measured. Every combination of gray, green, red, and blue as figure and/or ground was tested. Compared with gray and green light, fade times were greatest when red or blue light either strengthened the figure signal by reducing M activity in the figure, or weakened the ground signal by reducing M activity in ground. The results support a dynamic antagonistic relationship between M and P activity contributing to figure–ground perception as envisioned in Weisstein’s model.

Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.

Numerous studies have shown that more visual features can be stored in visual short-term memory (VSTM) when they are encoded from fewer objects (Luck & Vogel, 1997, Nature, 390, 279-281; Olson & Jiang, 2002, Perception & Psychophysics, 64[7], 1055-1067). This finding has been consistent for simple objects with one surface and one boundary contour, but very few experiments have shown a clear performance benefit when features are organized as multipart objects versus spatially dispersed single-feature objects. Some researchers have suggested multipart object integration is not mandatory because of the potential ambiguity of the display (Balaban & Luria, 2015, Cortex, 26(5), 2093-2104; Luria & Vogel, 2014, Journal of Cognitive Neuroscience, 26[8], 1819-1828). For example, a white bar across the middle of a red circle could be interpreted as two objects, a white bar occluding a red circle, or as a single two-colored object. We explore whether an object benefit can be found by disambiguating the figure-ground organization of multipart objects using a luminance gradient and linear perspective to create the appearance of a unified surface. Also, we investigated memory for objects with a visual feature indicated by a hole, rather than an additional surface on the object. Results indicate the organization of multipart objects can influence VSTM performance, but the effect is driven by how the specific organization allows for use of global ensemble statistics of the memory array rather than a memory benefit for local object representations.

To compare the performance of deep learning classifiers for the diagnosis of plus disease in retinopathy of prematurity (ROP) trained using 2 methods for developing models on multi-institutional data sets: centralizing data versus federated learning (FL) in which no data leave each institution.Evaluation of a diagnostic test or technology.Deep learning models were trained, validated, and tested on 5255 wide-angle retinal images in the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. All images were labeled for the presence of plus, preplus, or no plus disease with a clinical label and a reference standard diagnosis (RSD) determined by 3 image-based ROP graders and the clinical diagnosis.We compared the area under the receiver operating characteristic curve (AUROC) for models developed on multi-institutional data, using a central approach initially, followed by FL, and compared locally trained models with both approaches. We compared the model performance (κ) with the label agreement (between clinical and RSD), data set size, and number of plus disease cases in each training cohort using the Spearman correlation coefficient (CC).Model performance using AUROC and linearly weighted κ.Four settings of experiment were used: FL trained on RSD against central trained on RSD, FL trained on clinical labels against central trained on clinical labels, FL trained on RSD against central trained on clinical labels, and FL trained on clinical labels against central trained on RSD (P = 0.046, P = 0.126, P = 0.224, and P = 0.0173, respectively). Four of the 7 (57%) models trained on local institutional data performed inferiorly to the FL models. The model performance for local models was positively correlated with the label agreement (between clinical and RSD labels, CC = 0.389, P = 0.387), total number of plus cases (CC = 0.759, P = 0.047), and overall training set size (CC = 0.924, P = 0.002).We found that a trained FL model performs comparably to a centralized model, confirming that FL may provide an effective, more feasible solution for interinstitutional learning. Smaller institutions benefit more from collaboration than larger institutions, showing the potential of FL for addressing disparities in resource access.

Retrospective evaluation of a deep learning-derived retinopathy of prematurity (ROP) vascular severity score in an operational ROP screening program demonstrated high diagnostic performance for detection of type 2 or worse ROP. To our knowledge, this is the first report in the literature that evaluated the use of artificial intelligence for ROP screening and represents a proof of concept. With further prospective validation, this technology might improve the accuracy, efficiency, and objectivity of diagnosis and facilitate earlier detection of disease progression in patients with potentially blinding ROP.

Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture.We performed a retrospective review of patients with intracranial aneurysms detected on vascular imaging at our institution between 2002 and 2018. The dataset was used to train 3 ML models (random forest, linear support vector machine [SVM], and radial basis function kernel SVM). Relative contributions of individual predictors were derived from the linear SVM model.Complete data were available for 845 aneurysms in 615 patients. Ruptured aneurysms (n = 309, 37%) were larger (mean 6.51 mm vs. 5.73 mm; P = 0.02) and more likely to be in the posterior circulation (20% vs. 11%; P0.001) than unruptured aneurysms. Area under the receiver operating curve was 0.77 for the linear SVM, 0.78 for the radial basis function kernel SVM models, and 0.81 for the random forest model. Aneurysm location and size were the 2 features that contributed most significantly to the model. Posterior communicating artery, anterior communicating artery, and posterior inferior cerebellar artery locations were most highly associated with rupture, whereas paraclinoid and middle cerebral artery locations had the strongest association with unruptured status.ML models are capable of accurately distinguishing ruptured from unruptured aneurysms and identifying features associated with rupture. Consistent with prior studies, location and size show the strongest association with aneurysm rupture.

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

Utilizing 23 datasets, we report a meta-analysis of an asymmetry in presaccadic fixation durations for saccades directed above and below eye fixation during saccadic exploration. For inclusion in the meta-analysis, saccadic exploration of complex visual displays had to have been made without gaze-contingent manipulations. Effect sizes for the asymmetry were quantified as Hedge’s g. Pooled effect sizes indicated significant asymmetries such that during saccadic exploration in a variety of tasks, presaccadic fixation durations for saccades directed into the upper visual field were reliably shorter than presaccadic fixation durations for saccades into the lower visual field. It is contended that the asymmetry is robust and important for efforts aimed at modelling when a saccade is initiated as a function of ensuing saccade direction.

The watercolor illusion (WCI) occurs when a physically non-colored region surrounded by contrasting contour and fringe appears filled in with a hue similar to the fringe. The present experiments explored how local and global stimulus factors influence the spatial expanse of WCI color spreading. Experiment 1 utilized two- and three-dimensional-appearing stimuli with the WCI in only one part of each stimulus. Some conditions fully enclosed the color-spreading region with fringe on all sides. Others removed fringe from one side, opening up the color-spreading region to another part of the stimulus. Regardless of perceived dimensionality or enclosure, color did not spread beyond the fringed color-spreading region as confirmed by illusion magnitude ratings and handwritten shading. Experiment 2 consisted of transparent "wireframe" versions of the opaque-appearing stimuli used in Experiment 1. This altered the local context by adding physical contours inside the fringed color-spreading region. As in Experiment 1, color did not spread beyond physically open regions. Furthermore, illusory color filled a space bound by a combination of physical and illusory contours depending on the fringe end-cuts and other perceptual organization cues within the stimulus. Our main focus in these experiments was to determine where color spreads in a variety of contexts. Perceptual organization factors other than perceived depth seem more likely to impact the spatial expanse of WCI color spreading. These are some of the first experiments to explore the impact of changes to local and global context on the spatial expanse of the WCI.