scholarly journals A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhao Shi ◽  
Chongchang Miao ◽  
U. Joseph Schoepf ◽  
Rock H. Savage ◽  
Danielle M. Dargis ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Intracranial Aneurysm ◽  
Computed Tomography Angiography ◽  
Good Tolerance ◽  
Common Life ◽  
Life Threatening ◽  
Lesion Level ◽  
A Prospective Study ◽  
Deep Learning Model

AbstractIntracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment.

scholarly journals Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

2020 ◽  
Author(s):  
Zhao Shi ◽  
Chongchang Miao ◽  
Chengwei Pan ◽  
Xue Chai ◽  
Xiu Li Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intracranial Aneurysm ◽  
High Confidence ◽  
Common Life ◽  
Bone Removal ◽  
Life Threatening ◽  
Diagnosis Tool ◽  
Aneurysm Detection ◽  
A Prospective Study ◽  
Human Burden

AbstractIntracranial aneurysm is a common life-threatening disease. CTA is recommended as a standard diagnosis tool, while the interpretation is time-consuming and challenging. We presented a novel deep-learning-based framework trained on 1,177 DSA verified bone-removal CTA cases. The framework had excellent tolerance to the influence of occult cases of CTA-negative but DSA-positive aneurysms, image quality, and manufacturers. Simulated real-world studies were conducted in consecutive internal and external cohorts, achieving improved sensitivity and negative predictive value than radiologists. A specific cohort of suspected acute ischemic stroke was employed and found 96.8% predicted-negative cases can be trusted with high confidence, leading to reducing in human burden. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to radiologists’ assessment.

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.

“On the Spot”: The Use of Four-Dimensional Computed Tomography Angiography to Differentiate a True Spot Sign From a Distal Intracranial Aneurysm

2017 ◽  
Vol 105 ◽  
pp. 1037.e9-1037.e12 ◽  
Author(s):  
Johanna P. de Jong ◽  
Leo Kluijtmans ◽  
Martinus J. van Amerongen ◽  
Mathias Prokop ◽  
Hieronymus D. Boogaarts ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Intracranial Aneurysm ◽  
Computed Tomography Angiography ◽  
Spot Sign

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 Computed tomography angiography findings of abdominal aortic disease: A review of emergencies

2020 ◽  
Vol 73 (11-12) ◽  
pp. 393-400
Author(s):  
Mirela Jukovic ◽  
Aleksandra Mijatovic ◽  
Ivana Stojic ◽  
Ljiljana Drazetin ◽  
Maja Stankov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Abdominal Aorta ◽  
Computed Tomography Angiography ◽  
Clinical Manifestations ◽  
Aortic Disease ◽  
Occlusive Disease ◽  
Tunica Media ◽  
Abdominal Aortic ◽  
Wide Range ◽  
Life Threatening

Introduction. The aorta is a major blood vessel that supplies all segments of the human body. Acute aortic syndrome is a term that implies a life-threatening aortic disease. Due to the speed of examination and widespread availability, computed tomography angiography is a front-line diagnostic modality for emergencies and diseases of the abdominal aorta. The aim of this study was to provide a wide range of potentially life-threatening abnormalities of the abdominal aorta in daily clinical and radiological practice through a series of computed tomography angiography images and three-dimensional virtual reconstruction. Abdominal aortic aneurysm is defined as a 50% increase in diameter more than the normal arterial diameter. One of the most important complications of an aneurysm is a rupture that can be acute or chronic, presenting with various clinical manifestations. Aortic dissections are caused by abnormality of the tunica media layer, forming an intimal-medial flap and two types of lumen. A penetrating aortic ulcer may erode through the internal elastic lamina of the aortic wall and allow formation of hematoma within the tunica media. Occlusive disease of the abdominal aorta may refer to the late stage of chronic aortoiliac occlusive disease, whereas the acute and/or subacute form occurs due to sudden thrombosis or occlusion. Conclusion. The recognition of specific radiological signs of abdominal aortic disease using computed tomography angiography contributes to optimal treatment of patients and reduces mortality.

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