Abstract
Cardiovascular disease is a high-fatality illness. Intravascular Optical Coherence Tomography (IVOCT) technology can significantly assist in diagnosing and treating cardiovascular diseases. However, locating and classifying lesions from hundreds of IVOCT images is time-consuming and challenging, especially for junior physicians. An automatic lesion detection and classification model is desirable. To achieve this goal, in this work, we first collect an IVOCT dataset, including 2,988 images from 69 IVOCT data and 4,734 annotations of lesions spanning over three categories. Based on the newly-collected dataset, we propose a multi-class detection model based on Vision Transformer, called G-Swin Transformer. The essential part of our model is grid attention which is used to model relations among consecutive IVOCT images. Through extensive experiments, we show that the proposed G-Swin Transformer can effectively localize different types of lesions in IVOCT images, significantly outperforming baseline methods in all evaluation metrics. Our code is available via this link. https://github.com/Shao1Fan/G-Swin-Transformer
Z. Wang and Y. Shao—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Murphy, S., Xu, J., Kochanek, K., Arias, E., Tejada-Vera, B.: Deaths: final data for 2018 (2021)
Virani, S., et al.: Heart disease and stroke statistics-2021 update: a report from the American heart association. Circulation. 143, e254–e743 (2021)
Huang, D., et al.: Optical coherence tomography. Science 254, 1178–1181 (1991)
Bezerra, H., Costa, M., Guagliumi, G., Rollins, A., Simon, D.: Intracoronary optical coherence tomography: a comprehensive review: clinical and research applications. JACC: Cardiovas. Interv. 2, 1035–1046 (2009)
Jia, H., et al.: In vivo diagnosis of plaque erosion and calcified nodule in patients with acute coronary syndrome by intravascular optical coherence tomography. J. Am. Coll. Cardiol. 62, 1748–1758 (2013)
Li, C., et al.: Comprehensive assessment of coronary calcification in intravascular OCT using a spatial-temporal encoder-decoder network. IEEE Trans. Med. Imaging 41, 857–868 (2021)
Liu, X., Du, J., Yang, J., Xiong, P., Liu, J., Lin, F.: Coronary artery fibrous plaque detection based on multi-scale convolutional neural networks. J. Signal Process. Syst. 92, 325–333 (2020)
Gessert, N., et al.: Automatic plaque detection in IVOCT pullbacks using convolutional neural networks. IEEE Trans. Med. Imaging 38, 426–434 (2018)
Cao, X., Zheng, J., Liu, Z., Jiang, P., Gao, D., Ma, R.: Improved U-net for plaque segmentation of intracoronary optical coherence tomography images. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12893, pp. 598–609. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86365-4_48
Regar, E., Ligthart, J., Bruining, N., Soest, G.: The diagnostic value of intracoronary optical coherence tomography. Herz: Kardiovaskulaere Erkraenkungen 36, 417–429 (2011)
Kubo, T., Xu, C., Wang, Z., Ditzhuijzen, N., Bezerra, H.: Plaque and thrombus evaluation by optical coherence tomography. Int. J. Cardiovasc. Imaging 27, 289–298 (2011)
Falk, E., Nakano, M., Bentzon, J., Finn, A., Virmani, R.: Update on acute coronary syndromes: the pathologists’ view. Eur. Heart J. 34, 719–728 (2013)
Saw, J.: Spontaneous coronary artery dissection. Can. J. Cardiol. 29, 1027–1033 (2013)
Pepe, A., et al.: Detection, segmentation, simulation and visualization of aortic dissections: a review. Med. Image Anal. 65, 101773 (2020)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances In Neural Information Processing Systems, vol. 28 (2015)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. ArXiv Preprint ArXiv:1804.02767 (2018)
Jocher, G.: YOLOv5 by ultralytics (2020). https://github.com/ultralytics/yolov5
Lin, T., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 2980–2988 (2017)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference On Computer Vision, pp. 10012–10022 (2021)
Acknowledgement
This work is supported by the National Key Research and Development Project of China (No. 2022ZD0117801).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Z. et al. (2023). Vision Transformer Based Multi-class Lesion Detection in IVOCT. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-43987-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43986-5
Online ISBN: 978-3-031-43987-2
eBook Packages: Computer ScienceComputer Science (R0)
