Abstract
Coronary atherosclerosis is a leading cause of morbidity and mortality worldwide. It is often treated by placing stents in the coronary arteries. Inappropriately placed stents or malappositions can result in post-interventional complications. Intravascular Ultrasound (IVUS) imaging offers a potential solution by providing real-time endovascular guidance for stent placement. The signature of malapposition is very subtle and requires exploring second-order relationships between blood flow patterns, vessel walls, and stents. In this paper, we perform a comparative study of various deep learning methods and their feature extraction capabilities for building a malapposition detector. Our results in the study address the importance of incorporating domain knowledge in performance improvement while still indicating the limitations of current systems for achieving clinically ready performance.
This work was funded in part by MIT-IBM Watson AI Lab.
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References
Arora, P., Singh, P., Girdhar, A., Vijayvergiya, R.: A state-of-the-art review on coronary artery border segmentation algorithms for intravascular ultrasound (IVUS) images. Cardiovasc. Eng. Technol. 14, 1–32 (2023)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Banerjee, S., Alaiti, A.: Complications of percutaneous coronary interventions in calcified lesions: causes, recognition, management, and how to avoid. In: Debulking in Cardiovascular Interventions and Revascularization Strategies, pp. 311–319. Elsevier (2022)
Benjamin, E.J., et al.: Heart disease and stroke statistics-2018 update: a report from the American heart association. Circulation 137(12), e67–e492 (2018)
Blanco, P.J., et al.: Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. Med. Image Anal. 75, 102262 (2022)
Brown, G., Pocock, A., Zhao, M.J., Luján, M.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J. Mach. Learn. Res. 13, 27–66 (2012)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)
Cong, Y., Wang, S., Liu, J., Cao, J., Yang, Y., Luo, J.: Deep sparse feature selection for computer aided endoscopy diagnosis. Pattern Recogn. 48(3), 907–917 (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Huang, C., Wang, J., Xie, Q., Zhang, Y.D.: Analysis methods of coronary artery intravascular images: a review. Neurocomputing 489, 27–39 (2022)
Iakubovskii, P.: Segmentation models pytorch (2019). https://github.com/qubvel/segmentation_models.pytorch
Jia, Y., Huang, C., Darrell, T.: Beyond spatial pyramids: receptive field learning for pooled image features. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3370–3377. IEEE (2012)
Katouzian, A., Angelini, E.D., Carlier, S.G., Suri, J.S., Navab, N., Laine, A.F.: A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images. IEEE Trans. Inf Technol. Biomed. 16(5), 823–834 (2012)
Li, J., Wu, L., Wen, G., Li, Z.: Exclusive feature selection and multi-view learning for Alzheimer’s disease. J. Vis. Commun. Image Represent. 64, 102605 (2019)
Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. (CSUR) 50(6), 1–45 (2017)
Li, Y.C., Shen, T.Y., Chen, C.C., Chang, W.T., Lee, P.Y., Huang, C.C.J.: Automatic detection of atherosclerotic plaque and calcification from intravascular ultrasound images by using deep convolutional neural networks. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68(5), 1762–1772 (2021)
Liebson, P.R., Klein, L.W.: Intravascular ultrasound in coronary atherosclerosis: a new approach to clinical assessment. Am. Heart J. 123(6), 1643–1660 (1992)
Liu, S., et al.: Automated quantitative assessment of coronary calcification using intravascular ultrasound. Ultrasound Med. Biol. 46(10), 2801–2809 (2020)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Masuda, T., et al.: Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: validation using IB-IVUS. Radiography 28(1), 61–67 (2022)
Min, H.S., et al.: Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning. Cardiovasc. Intervent. 14(9), 1021–1029 (2021)
Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)
Ng, J.C.K., Shaoliang, S.L., Zhong, L., Collet, C., Foin, N., Ang, H.Y.: Stent malapposition generates stent thrombosis: insights from a thrombosis model. Int. J. Cardiol. 353, 43–45 (2022)
Roffo, G., Melzi, S., Cristani, M.: Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4202–4210 (2015)
Shinohara, H., et al.: Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries. PLoS ONE 16(8), e0255577 (2021)
Song, H.G., et al.: Intravascular ultrasound assessment of optimal stent area to prevent in-stent restenosis after zotarolimus-, everolimus-, and sirolimus-eluting stent implantation. Catheter. Cardiovasc. Interv. 83(6), 873–878 (2014)
Truesdell, A.G., et al.: Intravascular imaging during percutaneous coronary intervention: JACC state-of-the-art review. J. Am. Coll. Cardiol. 81(6), 590–605 (2023)
Wissel, T., et al.: Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks. J. Med. Imaging 9(2), 025001–025001 (2022)
Zou, Q., Ni, L., Zhang, T., Wang, Q.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015)
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Kashyap, S. et al. (2024). Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_17
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