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#CACSNet: Robust classification and segmentation of carotid artery calcification.

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network

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  • WHO. The top 10 causes of death. 2020. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.

  • Garoff, M. et al. Carotid calcification in panoramic radiographs: Radiographic appearance and the degree of carotid stenosis. Dentomaxillofac. Radiol. 45(6), 20160147 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gelabert, H. A. & Moore, W. S. Carotid endarterectomy: Current status. Curr. Probl. Surg. 28(3), 181–262 (1991).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jebari-Benslaiman, S. et al. Pathophysiology of atherosclerosis. Int. J. Mol. Sci. 23(6), 3346 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mughal, M. M. et al. Symptomatic and asymptomatic carotid artery plaque. Expert Rev. Cardiovasc. Ther. 9(10), 1315–1330 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Owen, D. R. et al. Imaging of atherosclerosis. Annu. Rev. Med. 62, 25–40 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Underhill, H. R. et al. MRI of carotid atherosclerosis: Clinical implications and future directions. Nat. Rev. Cardiol. 7(3), 165–173 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Wintermark, M. et al. High-resolution CT imaging of carotid artery atherosclerotic plaques. Am. J. Neuroradiol. 29(5), 875–882 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gaitini, D. & Soudack, M. Diagnosing carotid stenosis by Doppler sonography: State of the art. J. Ultrasound Med. 24(8), 1127–1136 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Nandalur, K. R. et al. Carotid artery calcification on CT may independently predict stroke risk. AJR Am. J. Roentgenol. 186(2), 547–552 (2006).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ghassemzadeh, S. et al. Incidental findings detected with panoramic radiography: Prevalence calculated on a sample of 2017 cases treated at a major Italian trauma and cancer centre. Oral Radiol. 37(3), 507–517 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Maia, P. R. L. et al. Presence and associated factors of carotid artery calcification detected by digital panoramic radiography in patients with chronic kidney disease undergoing hemodialysis. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 126(2), 198–204 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Constantine, S. et al. Carotid artery calcification on orthopantomograms (CACO study)—Is it indicative of carotid stenosis?. Aust. Dent. J. 64(1), 4–10 (2019).

    Article 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • Soares, G.-C. & Kurita, L.-M. Prevalence of carotid artery calcifications among 2,500 digital panoramic radiographs of an adult Brazilian population. Medicina Oral, Patologia Oral y Cirugia Bucal 23(3), e256 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Friedlander, A. H. & Lande, A. Panoramic radiographic identification of carotid arterial plaques. Oral Surg. Oral Med. Oral Pathol. 52(1), 102–104 (1981).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Carter, L. C. Discrimination between calcified triticeous cartilage and calcified carotid atheroma on panoramic radiography. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod. 90(1), 108–110 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Almog, D. M. et al. Evaluation of a training program for detection of carotid artery calcifications on panoramic radiographs. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod. 90(1), 111–117 (2000).

    Article 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • Rubiu, G. et al. Teeth segmentation in panoramic dental X-ray using mask regional convolutional neural network. Appl. Sci. 13(13), 7947 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Liu, X. et al. Advances in Deep Learning-Based Medical Image Analysis (Health Data Science, 2021).

  • Kats, L. et al. Atherosclerotic carotid plaque on panoramic radiographs: Neural network detection. Int. J. Comput. Dent. 22(2), 163–169 (2019).

    PubMed 

    Google Scholar
     

  • Amitay, M. et al. Deep convolution neural network for screening carotid calcification in dental panoramic radiographs. PLoS Digit. Health. 2(4), e0000081 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, R. et al. Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: Advantages of training with high-resolution imaging and histology. Front. Cardiovasc. Med. 10, 1127653 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, Y. et al. The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation. Front. Physiol. 13, 1057800 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deng, C. et al. Automatic segmentation of ultrasound images of carotid atherosclerotic plaque based on Dense-UNet. Technol. Health Care. 31(1), 165–179 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Raggi, P. & O’Neill, W. C. Imaging for vascular calcification. Semin. Dial. 30(4), 347–352 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Mujaj, B. et al. Comparison of CT and CMR for detection and quantification of carotid artery calcification: The Rotterdam Study. J. Cardiovasc. Magn. Reson. 19, 1–7 (2017).


    Google Scholar
     

  • Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

  • Howard, A.G. et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).

  • He, K. et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016).

  • Huang, G. et al. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2017).

  • Tan, M., Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (PMLR, 2019).

  • Hu, J., Shen, L., Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2018).

  • Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015).

    Article 
    MathSciNet 

    Google Scholar
     

  • Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009).

    Article 

    Google Scholar
     

  • Salehi, S. S. M., Erdogmus, D., Gholipour, A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging (Springer, 2017).

  • Abraham, N., Khan, N. M. A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). (IEEE, 2019).

  • DeLong, E. R., DeLong, D. M., Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics. 837–845 (1988).

  • Adadi, A. & Berrada, M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018).

    Article 

    Google Scholar
     

  • Van der Velden, B. H. et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (2017).

  • Mortimer, R., Nachiappan, S. & Howlett, D. C. Carotid artery stenosis screening: Where are we now?. Br. J. Radiol. 91(1090), 20170380 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Friedlander, A. H. Identification of stroke-prone patients by panoramic and cervical spine radiography. Dentomaxillofac. Radiol. 24(3), 160–164 (1995).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yoon, S. J. et al. Diagnostic accuracy of panoramic radiography in the detection of calcified carotid artery. Dentomaxillofac. Radiol. 37(2), 104–108 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Janiszewska-Olszowska, J. et al. Carotid artery calcifications on panoramic radiographs. Int. J. Environ. Res. Public Health 19(21), 14056 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cha, J.-Y. et al. Panoptic segmentation on panoramic radiographs: Deep learning-based segmentation of various structures including maxillary sinus and mandibular canal. J. Clin. Medi. 10(12), 2577 (2021).

    Article 

    Google Scholar
     

  • Nasseh, I. & Aoun, G. Carotid artery calcification: A digital panoramic-based study. Diseases 6(1), 15 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yoon, S.-J. et al. Interobserver agreement on the diagnosis of carotid artery calcifications on panoramic radiographs. Imaging Sci. Dent. 44(2), 137–141 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alves, N., Deana, N. F. & Garay, I. Detection of common carotid artery calcifications on panoramic radiographs: Prevalence and reliability. Int. J. Clin. Exp. Med. 7(8), 1931 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rumberger, J. A. et al. Coronary artery calcium area by electron-beam computed tomography and coronary atherosclerotic plaque area. A histopathologic correlative study. Circulation 92(8), 2157–2162 (1995).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bastos, J. et al. Sensitivity and accuracy of panoramic radiography in identifying calcified carotid atheroma plaques. Braz. J. Oral Sci. 11, 88–93 (2012).


    Google Scholar
     

  • Zhang, L. et al. Advances in CT techniques in vascular calcification. Front. Cardiovasc. Med. 8, 716822 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shinjo, K., et al. A detection method for carotid artery calcification in dental panoramic radiographs. In International Workshop on Smart Info-Media Systems in Asia 4 (2009).

  • Harada, H., et al. Improved detection method for carotid artery calcification in dental panoramic radiographs considering local features. In 2013 International Symposium on Intelligent Signal Processing and Communication Systems (2013).

  • Sawagashira, T. et al. An automatic detection method for carotid artery calcifications using top-hat filter on dental panoramic radiographs. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2011, 6208–6211 (2011).

    PubMed 

    Google Scholar
     

  • Meshram, N. H. et al. Deep learning for carotid plaque segmentation using a dilated U-Net architecture. Ultrason. Imaging 42(4–5), 221–230 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, R. et al. Deep learning-based carotid plaque segmentation from B-Mode ultrasound images. Ultrasound Med. Biol. 47(9), 2723–2733 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Jain, P. K. et al. Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound. Comput. Biol. Med. 136, 104721 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ronneberger, O., Fischer, P., Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 (Springer, 2015).

  • Li, Z., Kamnitsas, K. & Glocker, B. Analyzing overfitting under class imbalance in neural networks for image segmentation. IEEE Trans. Med. Imaging 40(3), 1065–1077 (2020).

    Article 

    Google Scholar
     

  • Hashemi, S. R. et al. Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection. IEEE Access 7, 1721–1735 (2018).

    Article 

    Google Scholar
     

  • Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019).

    Article 

    Google Scholar
     

  • Naqvi, T. Z. & Lee, M. S. Carotid intima-media thickness and plaque in cardiovascular risk assessment. JACC Cardiovasc. Imaging 7(10), 1025–1038 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Alman, A. C. et al. Validation of a method for quantifying carotid artery calcification from panoramic radiographs. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 116(4), 518–524 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Wannarong, T. et al. Progression of carotid plaque volume predicts cardiovascular events. Stroke 44(7), 1859–1865 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Spence, J. D. et al. Carotid plaque area: A tool for targeting and evaluating vascular preventive therapy. Stroke 33(12), 2916–2922 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Lu, M. et al. Shape and location of carotid atherosclerotic plaque and intraplaque hemorrhage: A high-resolution magnetic resonance imaging study. J. Atheroscler. Thromb. 26(8), 720–727 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mackinnon, A. D. et al. Rates and determinants of site-specific progression of carotid artery intima-media thickness: The carotid atherosclerosis progression study. Stroke 35(9), 2150–2154 (2004).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Adams, G. J. et al. Tracking regression and progression of atherosclerosis in human carotid arteries using high-resolution magnetic resonance imaging. Magn. Reson. Imaging 22(9), 1249–1258 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Kamikawa, R. S. et al. Study of the localization of radiopacities similar to calcified carotid atheroma by means of panoramic radiography. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod. 101(3), 374–378 (2006).

    Article 
    PubMed 

    Google Scholar
     

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    Source link: https://www.nature.com/articles/s41598-024-64265-4

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