A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning
Wing Keung Cheung,Robert G. Bell,Arjun Nair,Leon Menezes,Riyaz S. Patel,Simon Wan,Kacy Chou,Jiahang Chen,Ryo Torii,Rhodri H Davies,James C. Moon,Daniel C. Alexander,Joseph Jacob +12 more
TLDR
In this paper, a fully automatic two-dimensional Unet model is proposed to segment the aorta and coronary arteries on CTCA images, which achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively.Abstract:
Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA 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. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.read more
Citations
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Journal ArticleDOI
Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique
TL;DR: The present work is unique in applying a feature extraction model with CNN for CAD detection and the performance of the proposed feature extraction and CNN model is superior to the existing models.
Journal ArticleDOI
Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET
TL;DR: In this article , the modified and improved models of UNET suitable for increasing segmentation accuracy were introduced for MRI image segmentation, which is a well-known semantic segmentation technique in medical image analysis.
Journal ArticleDOI
Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data
Hyeong Jun Lee,Young-Woo Kim,Jun-Hong Kim,Yong Joon Lee,Jin Yong Moon,Professor Jeong,Jang Bong Jeong,Jung Sun Kim,J. Lee +8 more
TL;DR: In this paper , an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation, was developed, which consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features.
Journal ArticleDOI
UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias
Jasjit S. Suri,Mrinalini Bhagawati,Sushant Agarwal,Sudip Paul,Amit Pandey,Suneet K. Gupta,Luca Saba,Kosmas I. Paraskevas,Narendra N. Khanna,John R. Laird,Amer M. Johri,Manudeep Kalra,Mostafa M. Fouda,Mostafa Fatemi,Subbaram Naidu +14 more
TL;DR: In this paper , the authors presented a review of UNet-based segmentation methods by complexity, complexity, pruning, and AI bias, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet based architecture, and interfacing the three facets of AI, the pruning and explainable AI (XAI), and the AI bias.
Journal ArticleDOI
UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias
TL;DR: In this article , the authors presented a review of UNet-based segmentation methods by complexity, complexity, pruning, and AI bias, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet based architecture, and interfacing the three facets of AI, the pruning and explainable AI (XAI), and the AI bias.
References
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