scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon J. Menezes ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Computationally Efficient ◽  
Efficient Approach

scholarly journals A Lucas–Lehmer approach to generalised Lebesgue–Ramanujan–Nagell equations

2021 ◽  
Author(s):  
Vandita Patel
Keyword(s):  
Computationally Efficient ◽  
Efficient Approach ◽  
Coprime Integers ◽  
Primitive Divisor

AbstractWe describe a computationally efficient approach to resolving equations of the form $$C_1x^2 + C_2 = y^n$$ C 1 x 2 + C 2 = y n in coprime integers, for fixed values of $$C_1$$ C 1 , $$C_2$$ C 2 subject to further conditions. We make use of a factorisation argument and the Primitive Divisor Theorem due to Bilu, Hanrot and Voutier.

scholarly journals A computationally efficient approach for template matching-based image registration

Sadhana ◽  
2014 ◽  
Vol 39 (2) ◽  
pp. 317-331 ◽  
Author(s):  
VILAS H GAIDHANE ◽  
YOGESH V HOTE ◽  
VIJANDER SINGH
Keyword(s):  
Image Registration ◽  
Template Matching ◽  
Computationally Efficient ◽  
Efficient Approach

scholarly journals A computationally efficient approach for calculating galaxytwo-point correlations

2018 ◽  
Vol 480 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Regina Demina ◽  
Sanha Cheong ◽  
Segev BenZvi ◽  
Otto Hindrichs
Keyword(s):  
Computationally Efficient ◽  
Efficient Approach

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

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