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.

Vessel segmentation is essential for clinical diagnosis and surgical planning. However, it is quite challenging for automatic blood vessel segmentation due to low contrast, complex structure, and variable scale, especially when the annotated data is scarce. In this paper, we propose a novel multiscale attention aggregation network (MAA-Net) for vessel segmentation. In MAA-Net, based on a U-shaped encoder-decoder architecture, the dual attention module with scale factors is employed behind the decoder at each stage to generate multi-resolution feature maps adaptively weighted by channel and location attention. In this way, multiscale contextual information with long-range dependencies can be captured to tackle scale variations of vessels. Meanwhile, these attention feature maps are gradually integrated into multi-level aggregate supervision to assemble multiscale context information for refining segmentation results. The proposed method was evaluated on the retinal vessel and coronary angiography dataset (DRIVE and DCA1). Results demonstrate that MAA-Net achieves state-of-the-art performance for vessel segmentation. The code will be available at: https://github.com/lseventeen/MAA-Net-Vessel-Segmentation.

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet.

Retinal vessel segmentation is of great significance for the clinical diagnosis of eye-related diseases and diabetic retinopathy. CNN-based models have led to advancements in the task of retinal vessel segmentation in recent years, but such methods typically miss high-frequency information such as object edges and delicate features, which are critical for vessel segmentation. Therefore, we present a Dual-stream Edge Complementary Network (DECNet) with a novel Edge Complementary Module (ECM) to better tackle these challenges. Specifically, in DECNet, an edge regression stream is added to regress the edges of vessels, and the regression result can be feedback to the original body stream to replenish the missing and coarse edges in the segmentation results. Moreover, the ECM is designed to enhance the interaction between the body stream and the edge stream, and exploit more complementary information. Extensive experiment results on DRIVE and CHASEDB1 not only demonstrate the effectiveness of the proposed DECNet but also indicate that our DECNet outperforms other state-of-the-art approaches.

Pedestrian detection based on visual sensors has made significant progress, in which region proposal is the key step. There are two mainstream methods to generate region proposals: anchor‐based and anchor‐free. However, anchor‐based methods need more hyperparameters related to anchors for training compared with anchor‐free methods. In this paper, we propose a novel multiscale anchor‐free (MSAF) region proposal network to obtain proposals, especially for small‐scale pedestrians. It usually has several branches to predict proposals and assigns ground truth according to the height of pedestrian. Each branch consists of two components: one is feature extraction, and the other is detection head. Adapted channel feature fusion (ACFF) is proposed to select features at different levels of the backbone to effectively extract features. The detection head is used to predict the pedestrian center location, center offsets, and height to get bounding boxes. With our classifier, the detection performance can be further improved, especially for small‐scale pedestrians. The experiments on the Caltech and CityPersons demonstrate that the MSAF can significantly boost the pedestrian detection performance and the log‐average miss rate (MR) on the reasonable setting is 3.97% and 9.5%, respectively. If proposals are reclassified with our classifier, MR is 3.38% and 8.4%. The detection performance can be further improved, especially for small‐scale pedestrians.

Serverless computing is a new cloud programming and deployment paradigm that is receiving wide-spread uptake. Serverless offerings such as Amazon Web Services (AWS) Lambda, Google Functions, and Azure Functions automatically execute simple functions uploaded by developers, in response to cloud-based event triggers. The serverless abstraction greatly simplifies integration of concurrency and parallelism into cloud applications, and enables deployment of scalable distributed systems and services at very low cost. Although a significant first step, the serverless abstraction requires tools that software engineers can use to reason about, debug, and optimize their increasingly complex, asynchronous applications. Toward this end, we investigate the design and implementation of GammaRay, a cloud service that extracts causal dependencies across functions and through cloud services, without programmer intervention. We implement GammaRay for AWS Lambda and evaluate the overheads that it introduces for serverless micro-benchmarks and applications written in Python.