OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios

Modern approaches for vision-centric environment perception for autonomous navigation make extensive use of self-supervised monocular depth estimation algorithms that output disparity maps. However, when this disparity map is projected onto 3D space, the errors in disparity are magnified, resulting in a depth estimation error that increases quadratically as the distance from the camera increases. Though Light Detection and Ranging (LiDAR) can solve this issue, it is expensive and not feasible for many applications. To address the challenge of accurate ranging with low-cost sensors, we propose, OCTraN, a transformer architecture that uses iterative-attention to convert 2D image features into 3D occupancy features and makes use of convolution and transpose convolution to efficiently operate on spatial information. We also develop a self-supervised training pipeline to generalize the model to any scene by eliminating the need for LiDAR ground truth by substituting it with pseudo-ground truth labels obtained from boosted monocular depth estimation.Comment: This work was accepted as a spotlight presentation at the Transformers for Vision Workshop @CVPR 202

Use of Cooking Fuels and Cataract in a Population-Based Study: The India Eye Disease Study.

BACKGROUND: Biomass cooking fuels are commonly used in Indian households, especially by the poorest socioeconomic groups. Cataract is highly prevalent in India and the major cause of vision loss. The evidence on biomass fuels and cataract is limited. OBJECTIVES: To examine the association of biomass cooking fuels with cataract and type of cataract. METHODS: We conducted a population-based study in north and south India using randomly sampled clusters to identify people ≥ 60 years old. Participants were interviewed and asked about cooking fuel use, socioeconomic and lifestyle factors and attended hospital for digital lens imaging (graded using the Lens Opacity Classification System III), anthropometry, and blood collection. Years of use of biomass fuels were estimated and transformed to a standardized normal distribution. RESULTS: Of the 7,518 people sampled, 94% were interviewed and 83% of these attended the hospital. Sex modified the association between years of biomass fuel use and cataract; the adjusted odds ratio (OR) for a 1-SD increase in years of biomass fuel use and nuclear cataract was 1.04 (95% CI: 0.88, 1.23) for men and 1.28 (95% CI: 1.10, 1.48) for women, p interaction = 0.07. Kerosene use was low (10%). Among women, kerosene use was associated with nuclear (OR = 1.76, 95% CI: 1.04, 2.97) and posterior subcapsular cataract (OR = 1.71, 95% CI: 1.10, 2.64). There was no association among men. CONCLUSIONS: Our results provide robust evidence for the association of biomass fuels with cataract for women but not for men. Our finding for kerosene and cataract among women is novel and requires confirmation in other studies. Citation: Ravilla TD, Gupta S, Ravindran RD, Vashist P, Krishnan T, Maraini G, Chakravarthy U, Fletcher AE. 2016. Use of cooking fuels and cataract in a population-based study: the India Eye Disease Study. Environ Health Perspect 124:1857-1862; http://dx.doi.org/10.1289/EHP193

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Iron chelation abrogates excessive formation of hydroxyl radicals and lipid peroxidation products in monocytes of patients with Eales' disease: Direct evidence using electron spin resonance spectroscopy

10.1080/02713680490503723Current Eye Research286399-407CEYR