Deep Learning for Detection of Intracranial Aneurysms From Computed Tomography Angiography Images

2020 ◽  
Author(s):  
Xiujuan Liu ◽  
Jun Mao ◽  
Ning Sun ◽  
Xiangrong Yu ◽  
Lei Chai ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Computed Tomography Angiography ◽  
Intracranial Aneurysms

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fuguang Ji ◽  
Shuai Zhou ◽  
Zhangshuan Bi
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Computed Tomography Angiography ◽  
Atherosclerotic Lesion ◽  
Region Of Interest ◽  
Left Ventricular ◽  
Dice Similarity Coefficient ◽  
Imaging Features ◽  
Lesion Area ◽  
Imaging Characteristics

The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in diagnosing patients with atherosclerosis. 316 patients with atherosclerosis who were hospitalized for emergency treatment were treated with rapamycin (RAPA) in the hospital. A group of manually delineated left ventricular myocardia (LVM) on the patient’s coronary computed tomography angiography (CCTA) were selected as the region of interest for imaging features extracted. The CCTA images of 80% of patients were randomly selected for training, and those of 20% of patients were used for verification. The correlation matrix method was used to remove redundant image omics features under different correlation thresholds. In the validation set, CCTA diagnostic parameters were about 40 times higher than the manually segmented data. The average dice similarity coefficient was 91.6%. The proposed method also produced a very small centroid distance (mean 1.058 mm, standard deviation 1.245 mm) and volume difference (mean 1.640), with a segmentation time of about 1.45 ± 0.51 s, compared to about 744.8 ± 117.49 s for physician manual segmentation. Therefore, the deep learning model effectively segmented the atherosclerotic lesion area, measured and assisted the diagnosis of future atherosclerosis clinical cases, improved medical efficiency, and accurately identified the patient’s lesion area. It had great application potential in helping diagnosis and curative effect analysis of atherosclerosis.

scholarly journals Application of computed tomography angiography for evaluating clinical morphology in intracranial aneurysms – monocentric study

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051989479
Author(s):  
Ru-de Sui ◽  
Chun-guo Wang ◽  
Dong-wei Han ◽  
Xiu-qing Zhang ◽  
Qing Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Intracranial Aneurysms ◽  
Parent Artery ◽  
Neck Width ◽  
Aneurysm Size ◽  
Aneurysmal Neck ◽  
The Relationship ◽  
Accuracy Rates ◽  
Guiding Effect

Objective To examine the clinical effect of computed tomography angiography (CTA) on parameters of intracranial aneurysms in different locations and with different sizes using digital subtraction angiography (DSA) as the standard. Methods Patients with intracranial aneurysms who underwent CTA examinations at the same center and received DSA examinations within 3 days were analyzed retrospectively. The morphological parameters of the aneurysms and parent arteries were measured with these two methods. Results Mean aneurysm size and parent artery diameter were not different between CTA and DSA. The size of microaneurysms was significantly smaller with DSA than with CTA. The aneurysmal neck width was not different between CTA and DSA. DSA could clearly evaluate the relationship between the aneurysmal neck and the parent artery in all cases. However, CTA had a 90% accuracy rate of visualizing this relationship. Conclusion The accuracy rates of evaluating aneurysm size and the aneurysmal neck width and parent artery diameter are similar between CTA and DSA. A DSA examination is essential for evaluating the relationship among microaneurysms, the aneurysmal neck, and the parent artery. CTA is widely applied and more safe in clinical practice, while DSA has a better guiding effect than CTA for some complicated aneurysms.

Interrater Agreement in the Radiologic Characterization of Ruptured Intracranial Aneurysms Based on Computed Tomography Angiography

2017 ◽  
Vol 103 ◽  
pp. 876-882.e1 ◽  
Author(s):  
Nicolai Maldaner ◽  
Martin N. Stienen ◽  
Philippe Bijlenga ◽  
Davide Croci ◽  
Daniel W. Zumofen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Intracranial Aneurysms ◽  
Interrater Agreement ◽  
Ruptured Intracranial Aneurysms

Computed Tomography Angiography Evaluation of Risk Factors for Unstable Intracranial Aneurysms

2018 ◽  
Vol 115 ◽  
pp. e27-e32 ◽  
Author(s):  
Guang-xian Wang ◽  
Ming-fu Gong ◽  
Li Wen ◽  
Lan-lan Liu ◽  
Jin-bo Yin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Intracranial Aneurysms
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