Keywords
Stenosis Detection
Attention Mechanisms
X-ray Coronary Angiography
Convolutional Neural Networks
How to Cite
Abstract
Coronary stenosis results from unnatural narrowing of the heart arteries due to the accumulation of adipose depots, leading to different heart diseases and yielding top mortality worldwide. Thus far, deep learning-based methods for automatic stenosis over X-ray Coronary Angiography (XCA) have employed state-of-the-art architectures to solve the ImageNet challenge. With the advance of deep learning, contemporary architectures incorporated a variety of attention mechanisms to improve performance. Therefore, this paper presents a study of three attention mechanisms for stenosis detection in XCA images. Extensive experiments and comparisons over different Residual backbone networks are presented to verify the effectiveness of including such attention modules. An improvement of 4%, 10%, and 10% on the accuracy, recall, and F1-score was achieved using the approach, reaching mean values of 0.8787, 0.8610, and 0.8732, respectively.
References
Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, pages 2021; 1-22. https://doi.org/10.1007/s12065-020-00540-3.
Mohapatra S, Swarnkar, Das J. Deep convolutional neural network in medical image processing. In Handbook of deep learning in biomedical engineering. pages 25-60. Elsevier, 2021. https://doi.org/10.1016/B978-0-12-823014-5.00006-5.
Althnian A, AlSaeed D, Al-Baity H, Samha A, Bin Dris A, Alzakari N, et al. Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain. Applied Sciences, 2021; 11(2): 796. https://doi.org/10.3390/app11020796.
Hammernik K, Schlemper J, Qin C, Duan J, Summers RM, Rueckert D. Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination. Magnetic Resonance in Medicine, 2021; 86(4): 1859-1872. https://doi.org/10.1002/mrm.28827.
Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 2019; 6(1): 1-48. https://doi.org/10.1186/s40537-019-0197-0.
Yosinski J, Clune J, Bengio Y, Lipson H. How Transferable Are Features in Deep Neural Networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014; 2(14): 3320-3328. Montreal, Canada, dec MIT Press.
Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018; 7132-7141. Salt Lake City, UT, USA, Jun https://doi.org/10.1109/CVPR.2018.00745.
Woo S, Park J, Lee J-Y, Kweon IS. CBAM: Convolutional Block Attention Module. In Proceedings of the European Conference on Computer Vision, 2018; 3-19, Munich, Germany, https://doi.org/10.1007/978-3-030-01234-2 1.
Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020; 11531-11539, Seattle, WA, USA, Jun https://doi.org/10.1109/CVPR42600.2020.01155.
World Health Organization. Cardiovascular Diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/ cardiovascular-diseases-(cvds), Nov 2021.
Johal GS, Goel S, Kini A. Coronary Anatomy and Angiography. In Practical Manual of Interventional Cardiology, 2021; 35-49.
Manson EN, Ampoh VA, Fiagbedzi E, Amuasi JH, Flether JJ, Schandorf C. Image noise in radiography and tomography: Causes, effects and reduction techniques. Current Trends in Clinical & Medical Imaging, 2019; 2(5): 555620. https://doi.org/10.19080/CTCMI.2019.03.555620.
Chang C-F, Chang K-H, Lai C-H, Lin T-H, Liu T-J, Lee W-L, et al. Clinical outcomes of coronary artery bifurcation disease patients underwent Culotte two-stent technique: a single center experience. BMC Cardiovascular Disorders, 2019; 19(1): 1-8. https://doi.org/10.1186/s12872-019-1192-2.
Zhao C, Vij A, Malhotra S, Tang J, Tang H, Pienta D, et al. Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms. Computers in Biology and Medicine, 2021; 136: 104667. https://doi.org/10.1016/j.compbiomed.2021.104667.
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2018; 3-11. https://doi.org/10.1007/978-3-030-00889-5 1.
Cong C, Kato Y, Vasconcellos HD, Lima J, Venkatesh B. Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network. In International Conference on Bioinformatics and Biomedicine (BIBM), 2019; 1301-1308. San Diego, CA, USA, IEEE. https://doi.org/10.1109/BIBM47256.2019.8983033.
Wu W, Zhang J, Xie H, Zhao Y, Zhang S, Gu L. Automatic detection of coronary artery stenosis by convolutional neural network with temporal constraint. Computers in Biology and Medicine, 2020; 118: 103657. https://doi.org/10.1016/j.compbiomed.2020.103657.
Pang K, Ai D, Fang H, Fan J, Song H, Yang J. Stenosis-DetNet: Sequence consistency-based stenosis detection for X-ray coronary angiography. Computerized Medical Imaging and Graphics, 2021; 89: 101900. https://doi.org/10.1016/j.compmedimag.2021.101900.
Antczak K, Liberadzki L. Stenosis Detection with Deep Convolutional Neural Networks. In MATEC Web of Conferences, 2018; 210: 04001. EDP Sciences, https://doi.org/10.1051/matecconf/201821004001.
Ovalle-Magallanes El, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J. Transfer Learning for Stenosis Detection in X-ray Coronary Angiography. Mathematics, 2020; 8(9): 1510. https://doi.org/10.3390/math8091510.
Sameh S, AbdelAzim M, AbdelRaouf A. Narrowed coronary artery detection and classification using angiographic scans. In 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017; 73-79. Cairo, Egypt, IEEE. https://doi.org/10.1109/ICCES.2017.8275280.
Kishore AHN, Jayanthi VE. Automatic stenosis grading system for diagnosing coronary artery disease using coronary angiogram. International Journal of Biomedical Engineering and Technology, 2019; 31(3): 260-277. https://doi.org/10.1504/IJBET.2019.102974.
Wan T, Feng H, Tong C, Li D, Qin Z. Automated Identification and Grading of Coronary Artery Stenoses with X-ray Angiography. Computer Methods and Programs in Biomedicine, 2018; 167: 13-22. https://doi.org/10.1016/j.cmpb.2018.10.013.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, et al. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015; 1-9. IEEE Computer Society, 2015. https://doi.org/10.1109/CVPR.2015.7298594.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 2012; 25: 1097-1105. https://doi.org/10.1145/3065386.
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015; 1-14, URL http://arxiv.org/abs/1409.1556.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016; 770-778. IEEE Computer Society, 2016. https://doi.org/10.1109/CVPR.2016.90.
Zandigohar M, Erdogmus D, Schirner G. NetCut: Real-Time DNN Inference Using Layer Removal. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021; 1845-1850, Grenoble, France, IEEE. https://doi.org/10.23919/DATE51398.2021.9474052.
Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014; https://arxiv.org/ abs/1412.6980.
Loshchilov I, Hutter F. SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv preprint arXiv:1608.03983, 2016; https://arxiv.org/ abs/1608.03983.
Wightman R. PyTorch Image Models. https://github.com/rwightman/ pytorch-image-models, 2019.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In IEEE International Conference on Computer Vision (ICCV), 2017; 618-626, Venecia, Italia, oct 2017. IEEE Computer Society. https://doi.org/10.1109/ICCV.2017.74
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2022 Emmanuel Ovalle-Magallanes, Dora E. Alvarado-Carrillo, Juan Gabriel Avina-Cervantes, Ivan Cruz-Aceves, Jose Ruiz-Pinales, Jose Luis Contreras-Hernandez