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Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

Background:Nontraumatic spontaneous subarachnoid hemorrhage (SAH) is associated with a high mortality. This study was conducted to investigate the epidemiological features of nontraumatic spontaneous SAH in China.Methods:From January 2006 to December 2008, the clinical data of patients with nontraumatic SAH from 32 major neurosurgical centers of China were evaluated. Emergent digital subtraction angiography (DSA) was performed for the diagnosis of SAH sources in the acute stage of SAH (≤3 days). The results and complications of emergent DSA were analyzed. Repeated DSA or computed tomography angiography (CTA) was suggested 2 weeks later if initial angiographic result was negative.Results:A total of 2562 patients were enrolled, including 81.4% of aneurysmal SAH and 18.6% of nonaneurysmal SAH. The total complication rate of emergent DSA was 3.9% without any mortality. Among the patients with aneurysmal SAH, 321 cases (15.4%) had multiple aneurysms, and a total of 2435 aneurysms were detected. The aneurysms mostly originated from the anterior communicating artery (30.1%), posterior communicating artery (28.7%), and middle cerebral artery (15.9%). Among the nonaneurysmal SAH cases, 76.5% (n = 365) had negative initial DSA, including 62 cases with peri-mesencephalic nonaneurysmal SAH (PNSAH). Repeated DSA or CTA was performed in 252 patients with negative initial DSA, including 45 PNSAH cases. Among them, the repeated angiographic results remained negative in 45 PNSAH cases, but 28 (13.5%) intracranial aneurysms were detected in the remaining 207 cases. In addition, brain arteriovenous malformation (AVM, 7.5%), Moyamoya disease (7.3%), stenosis or sclerosis of the cerebral artery (2.7%), and dural arteriovenous fistula or carotid cavernous fistula (2.3%) were the major causes of nonaneurysmal SAH.Conclusions:DSA can be performed safely for pathological diagnosis in the acute stage of SAH. Ruptured intracranial aneurysms, AVM, and Moyamoya disease are the major causes of SAH detected by emergent DSA in China.