Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network

2020 ◽  
Vol 12 (10) ◽  
pp. 1023-1027
Author(s):  
Hailan Jin ◽  
Jiewen Geng ◽  
Yin Yin ◽  
Minghui Hu ◽  
Guangming Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Digital Subtraction Angiography ◽  
Network Structure ◽  
Intracranial Aneurysms ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Medical Image Segmentation ◽  
Digital Subtraction ◽  
Blind Test ◽  
End To End

BackgroundIntracranial aneurysms (IAs) are common in the population and may cause death.ObjectiveTo develop a new fully automated detection and segmentation deep neural network based framework to assist neurologists in evaluating and contouring intracranial aneurysms from 2D+time digital subtraction angiography (DSA) sequences during diagnosis.MethodsThe network structure is based on a general U-shaped design for medical image segmentation and detection. The network includes a fully convolutional technique to detect aneurysms in high-resolution DSA frames. In addition, a bidirectional convolutional long short-term memory module is introduced at each level of the network to capture the change in contrast medium flow across the 2D DSA frames. The resulting network incorporates both spatial and temporal information from DSA sequences and can be trained end-to-end. Furthermore, deep supervision was implemented to help the network converge. The proposed network structure was trained with 2269 DSA sequences from 347 patients with IAs. After that, the system was evaluated on a blind test set with 947 DSA sequences from 146 patients.ResultsOf the 354 aneurysms, 316 (89.3%) were successfully detected, corresponding to a patient level sensitivity of 97.7% at an average false positive number of 3.77 per sequence. The system runs for less than one second per sequence with an average dice coefficient score of 0.533.ConclusionsThis deep neural network assists in successfully detecting and segmenting aneurysms from 2D DSA sequences, and can be used in clinical practice.

scholarly journals An End to End Deep Neural Network for Iris Recognition

2020 ◽  
Vol 174 ◽  
pp. 505-517
Author(s):  
Qingqiao Hu ◽  
Siyang Yin ◽  
Huiyang Ni ◽  
Yisiyuan Huang
Keyword(s):  
Neural Network ◽  
Iris Recognition ◽  
Deep Neural Network ◽  
End To End

Intraoperative digital subtraction angiography and the surgical treatment of intracranial aneurysms and vascular malformations

1990 ◽  
Vol 73 (4) ◽  
pp. 526-533 ◽  
Author(s):  
Neil A. Martin ◽  
John Bentson ◽  
Fernando Viñuela ◽  
Grant Hieshima ◽  
Murray Reicher ◽  
...  
Keyword(s):  
Digital Subtraction Angiography ◽  
Intracranial Aneurysms ◽  
Vascular Malformations ◽  
Operating Table ◽  
Digital Subtraction ◽  
Intraoperative Angiography ◽  
Content Type ◽  
Head Holder ◽  
Cerebral Embolization ◽  
Leg Ischemia

✓ Intraoperative digital subtraction angiography using commercially available equipment was employed to confirm the precision of the surgical result in 105 procedures for intracranial aneurysms or arteriovenous malformations (AVM's). Transfemoral selective arterial catheterization was performed in most of these cases. A radiolucent operating table was used in all cases, and a radiolucent head-holder in most. In five of the 57 aneurysm procedures, clip repositioning was required after intraoperative angiography demonstrated an inadequate result. In five of the 48 AVM procedures, intraoperative angiography demonstrated residual AVM nidus which was then located and resected. In two cases intraoperative angiography failed to identify residual filling of an aneurysm which was seen later on postoperative angiography, and in one case the intraoperative study failed to demonstrate a tiny residual fragment of AVM which was seen on conventional postoperative angiography. Two complications resulted from intraoperative angiography: one patient developed aphasia from cerebral embolization and one patient developed leg ischemia from femoral artery thrombosis. This technique appears to be of particular value in the treatment of complex intracranial aneurysms and vascular malformations.

SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

2021 ◽  
Author(s):  
Yuheng Hu ◽  
Yili Hong
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Event Detection ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Neural Network Models ◽  
Neural Event ◽  
End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

PI-Net: An End-to-End Deep Neural Network for Bidirectionally and Directly Fusing Point Clouds With Images

2021 ◽  
Vol 6 (4) ◽  
pp. 8647-8654
Author(s):  
Qi Wang ◽  
Jian Chen ◽  
Jianqiang Deng ◽  
Xinfang Zhang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Point Clouds ◽  
End To End

AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT

2021 ◽  
Author(s):  
Dennis J. Lee ◽  
John Mulcahy-Stanislawczyk ◽  
Edward Jimenez ◽  
Derek West ◽  
Ryan Goodner ◽  
...  
Keyword(s):  
Neural Network ◽  
Iterative Reconstruction ◽  
Deep Neural Network ◽  
Sparse Data ◽  
End To End
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