Stroke is a leading cause of disability and fatality in the world, with ischemic stroke being the most common type. Digital Subtraction Angiography images, the gold standard in the operation process, can accurately show the contours and blood flow of cerebral vessels. The segmentation of cerebral vessels in DSA images can effectively help physicians assess the lesions. However, due to the disturbances in imaging parameters and changes in imaging scale, accurate cerebral vessel segmentation in DSA images is still a challenging task. In this paper, we propose a novel Edge Regularization Network (ERNet) to segment cerebral vessels in DSA images. Specifically, ERNet employs the erosion and dilation processes on the original binary vessel annotation to generate pseudo-ground truths of False Negative and False Positive, which serve as constraints to refine the coarse predictions based on their mapping relationship with the original vessels. In addition, we exploit a Hybrid Fusion Module based on convolution and transformers to extract local features and build long-range dependencies. Moreover, to support and advance the open research in the field of ischemic stroke, we introduce FPDSA, the first pixel-level semantic segmentation dataset for cerebral vessels. Extensive experiments on FPDSA illustrate the leading performance of our ERNet.

Ischemic stroke has a very high mortality and disability rate, and intracranial artery stenosis is an important cause of ischemic stroke. At present, transvascular interventional surgery is an effective remedy to treat intracranial artery stenosis, and as the gold standard in surgery, Digital Subtraction Angiography (DSA) images can effectively display the outline of blood vessels and the flow of blood. Detecting and locating the stenosis from DSA images is a challenging problem due to the large variation in the thickness of the blood vessel and the complex shape of the blood vessel. In this paper, we collect a dataset with 2860 DSA sequence samples and annotate stenosis locations, constructing the first automatic detection and localization method for stenosis in DSA images. In addition, considering that the commonly used Intersection-over-Union (IoU) loss ignores the similarity indicators of the image patches in the prediction box and the ground-truth (GT) box, a plug-and-play loss function that considers the image similarity between the prediction box and the GT box is proposed to effectively improve network performance. Extensive experiments demonstrate the effectiveness of our approach, which outperforms classical detectors.

Human-robot collaboration has been an important topic in the HRI communities. In this paper, we explore how robots can contribute to gas pipeline inspection work, and how they can support one of the most important elements of energy transportation infrastructure. To situate robots in the gas energy industry, we conducted a collaborative design study, where our co-designers were diverse stakeholders: from pipeline researchers to utility workers. The contribution of this paper is threefold: First, we explore gas pipeline work settings as a new context where robots can provide significant benefit, considering that public infrastructure is vast but understudied. Second, we collaboratively envisioned the design and use cases together with workers who are not often invited to human-robot collaboration research. Lastly, we address the importance of viewing humans in human-robot collaboration as “workers” whose roles and expertise are shaped within organizational dynamics. This study aims to shed light on the importance of a more nuanced understanding of work contexts and the positionality of robots within organizations.

The increasing fleet of aging aircrafts has resulted in an increasing demand for cost effective nondestructive evaluation (NDE) techniques that are accurate, reliable, and easy to use. Magneto-Optic Imaging (MOI) is such a technique, which has gained wide acceptance for detection of both surface and subsurface defects in multi-layer aircraft structures. The main advantage of MOI is rapid inspection and ease of interpreting image data in contrast to complex impedance signals from conventional eddy current instruments. One missing piece of the puzzle for advanced MOI systems is how to quantitatively analyse the MO images, and understand the detectability limits when image data are acquired under varying operational conditions. This paper presents a probability of detection (POD) study that is conducted using both simulation model-predicted and experimental MO image data. Simulated panels from a 3-D FEM model and experimental panels with machined defects are used to generate data for interpretation by human inspectors or automated systems, and subsequently for POD studies. The POD curves demonstrate the merits in optimizing inspection parameters that maximized the performance of current MOI systems. Parameters quantifying the detectability of MO image data using skewness functions are also presented and discussed.