Abstract
Vessel width estimation has a wide range of applications in disease diagnosis and treatment. In this paper, vessel width estimation is cast as a regression problem, and a novel Convolutional Neural Network (CNN) based method is proposed for vessel width estimation. In our CNN-based method, the idea of divide-and-conquer is introduced to solve the challenge of imbalanced training samples. Besides, in order to solve the shortage of training samples required by CNN, a vessel width label generation method is proposed to generate width labels from vessel segmentation labels. In the experiments, we apply our vessel width label generation method and CNN-based width estimation method to two tasks which are retinal vessel width estimation and coronary artery width estimation. Experimental results show that our width label generation method can generate sufficiently realistic width labels using accurate segmentation labels. Also, our CNN-based method can solve the challenge of imbalanced training samples, achieving state-of-the-art performance with less inference time.
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References
Klein, R., Klein, B.E., Moss, S.E.: The relation of systemic hypertension to changes in the retinal vasculature: the Beaver Dam Eye Study. Trans. Am. Ophthalmol. Soc. 95, 329–350 (1997)
Brinchmann-Hansen, O., Heier, H.: Theoretical relations between light streak characteristics and optical properties of retinal vessels. Acta Ophthalmol. 64, 33–37 (1986)
Rezaeian, M., Butlin, M., Golzan, S.M., Graham, S.L., Avolio, A.P.: A novel method for retinal vessel segmentation and diameter measurement using high speed video. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2781–2784 (2019)
Sun, G., Liu, X., Wang, S., Gao, L., Liu, M.: Width measurement for pathological vessels in retinal images using centerline correction and k-means clustering. Measurement 139, 185–195 (2019)
Zhou, L., Rzeszotarski, M.S., Singerman, L.J., Chokreff, J.M.: The detection and quantification of retinopathy using digital angiograms. IEEE Trans. Med. Imaging 13(4), 619–626 (1994)
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Kennedy, R.L.: Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans. Med. Imaging 23(10), 1196–1204 (2004)
Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009)
Xu, X., et al.: Vessel boundary delineation on fundus images using graph-based approach. IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011)
Li, Q., You, J., Zhang, D.: Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst. Appl. 39(9), 7600–7610 (2012)
Aliahmad, B., Kumar, D.K.: Adaptive Higuchi’s dimension-based retinal vessel diameter measurement. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1308–1311 (2016)
Huang, F., Dashtbozorg, B., Yeung, A.K.S., Zhang, J., Berendschot, T.T.J.M., ter Haar Romeny, B.M.: A comparative study towards the establishment of an automatic retinal vessel width measurement technique. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA -2017. LNCS, vol. 10554, pp. 227–234. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67561-9_26
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)
Al-Diri, B., Hunter, A., Steel, D., Habib, M., Hudaib, T., Berry, S.: REVIEW - a reference data set for retinal vessel profiles. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2262–2265 (2008)
Li, W., Lu, J., Feng, J., Xu, C., Zhou, J., Tian, Q.: BridgeNet: a continuity-aware probabilistic network for age estimation. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1145–1154 (2019)
Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: Proceedings of 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 742–751 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings on 3rd International Conference on Learning Representations (ICLR), pp. 1–15 (2015)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62073325, Grant 62003343, Grant U1913601, and Grant U20A20224; in part by the National Key Research and Development Program of China under Grant 2019YFB1311700; in part by the Youth Innovation Promotion Association of CAS under Grant 2020140; and in part by the Strategic Priority Research Program of CAS under Grant XDB32040000.
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Li, RQ. et al. (2021). Vessel Width Estimation via Convolutional Regression. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_58
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