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This article proposes an automated left ventricle (LV) segmentation model named Contextual-Feature-Induced Semantic Flow Propagation Network (CFISFPNet). The CFISFPNet leverages the convolutional GRU (ConvGRU) to extract contextual features and incorporates the optical flow technique to learn semantic flow information between adjacent MRI feature-maps. By propagating high-level semantic features into high-resolution feature-maps, CFISFPNet achieves accurate LV region segmentation at any phase in a cardiac cycle. Extensive confirmatory and comparative experiments have been conducted on three benchmark datasets: the Automated Cardiac Diagnosis Challenge, the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge, and the Toronto Children's dataset. The experimental results show that the CFISFPNet achieves Dice scores of 88.09%, 85.85%, and 86.99%, as well as Hausdorff distance metrics of 2.04, 2.27, and 2.07 on the respective datasets. These results outperform state-of-the-art approaches and approach manual segmentation performance of clinical experts. CFISFPNet exhibits significant potential in enhancing cardiovascular disease clinical diagnosis and treatment. • A context feature-induced semantic flow propagation network (CFISFPNet) is proposed. • CFISFPNet can segment left ventricle (LV) areas in whole-cardiac-cycle MRIs. • A ConvGRU is introduced to extract contextual features of LV images. • A semantic propagation module is proposed for contextual feature fusion. • CFISFPNet performs comparably and correlates well with manual segmentation results. [ABSTRACT FROM AUTHOR]

• Transit use of low-income groups drops off quickly as car ownership increases. • The elasticity of transit accessibility is the highest for low-income car-owners. • Most ridership growth from accessibility is predicted for car-deficit households. Transportation equity advocates recommend improving public transit in low-income neighbourhoods to alleviate socio-spatial inequalities and increase quality of life. However, transportation planners often overlook transit investments in neighbourhoods with "transit-captive" populations because they are assumed to result in less mode-shifting, congestion relief, and environmental benefits, compared to investments that aim to attract choice riders in wealthier communities. In North American cities, while many low-income households are already transit users, some also own and use private vehicles. It suggests that transit improvements in low-income communities could indeed result in more transit use and less car use. Accordingly, the main objective of this article is to explore the statistical relationship between transit use and transit accessibility as well as how this varies by household income and vehicle ownership in the Greater Toronto and Hamilton Area (GTHA). Using stratified regression models, we find that low-income households with one or more cars per adult have the most elastic relationship between transit accessibility and transit use; they are more likely to be transit riders if transit improves. However, we confirm that in auto-centric areas with poor transit, the transit use of low-income households drops off sharply as car ownership increases. On the other hand, a sensitivity analysis suggests more opportunities for increasing transit ridership among car-deficit households when transit is improved. These findings indicate that improving transit in low-income inner suburbs, where most low-income car-owning households are living, would align social with environmental planning goals. [ABSTRACT FROM AUTHOR]