Abstract
The detection of curvilinear structures in medical images, e.g., blood vessels or nerve fibers, is important in aiding management of many diseases. In this work, we propose a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography (OCT-A), color fundus image, and corneal confocal microscopy (CCM). Instead of the U-Net based convolutional neural network, we propose a novel network (CS-Net) which includes a self-attention mechanism in the encoder and decoder. Two types of attention modules are utilized - spatial attention and channel attention, to further integrate local features with their global dependencies adaptively. The proposed network has been validated on five datasets: two color fundus datasets, two corneal nerve datasets and one OCT-A dataset. Experimental results show that our method outperforms state-of-the-art methods, for example, sensitivities of corneal nerve fiber segmentation were at least 2% higher than the competitors. As a complementary output, we made manual annotations of two corneal nerve datasets which have been released for public access.
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References
Zhao, Y., et al.: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imag. 34(9), 1797–1807 (2015)
Zhao, Y., et al.: Automatic 2D/3D vessel enhancement in multiple modality images using a weighted symmetry filter. IEEE Trans. Med. Imag. 37(2), 438–450 (2018)
Fraz, M., et al.: Blood vessel segmentation methodologies in retinal images - a survey. Comput. Meth. Prog. Bio. 108, 407–433 (2012)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195
Cetin, S., Unal, G.: A higher-order tensor vessel tractography for segmentation of vascular structures. IEEE Trans. Med. Imag. 34, 2172–2185 (2015)
Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imag. 35, 2369–2380 (2016)
Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_16
Alom, M., et al.: Recurrent residual convolutional neural network based on U-net (R2U-Net) for medical image segmentation. arXiv:1802.06955 (2018)
Colonna, A., Scarpa, F., Ruggeri, A.: Segmentation of corneal nerves using a U-Net-based convolutional neural network. In: Stoyanov, D., et al. (eds.) OMIA/COMPAY -2018. LNCS, vol. 11039, pp. 185–192. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00949-6_22
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
Zhao, H., et al.: Pyramid scene parsing network. In: CVPR 2017, pp. 2281–2890 (2017)
Peng, C., et al.: Large kernel matters-improve semantic segmentation by global convolutional network. In: CVPR 2017, pp. 4353–4361 (2017)
Jun, F., et al.: Dual attention network for scene segmentation. In: CVPR 2019, pp. 3146–3154 (2019)
Azzopardi, G., et al.: Trainable cosfire filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)
Gu Z., et al.: CE-NET: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging (2019)
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-NET. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Oktay, O., et al.: Attention U-NET: learning where to look for the pancreas. arXiv:1804.03999 (2018)
Guimarães, P., et al.: A fast and efficient technique for the automatic tracing of corneal nerves in confocal microscopy. Trans. Vis. Sci. Technol. 5(5), 7 (2016)
Yokogawa, H., et al.: Mapping of normal corneal K-structures by in vivo laser confocal microscopy. Cornea 27, 879–883 (2008)
Acknowledgement
This work was supported by National Science Foundation Program of China (61601029, 61773297), Zhejiang Provincial Natural Science Foundation (LZ19F0 10001), and Ningbo Natural Science Foundation (2018A610055).
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Mou, L. et al. (2019). CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_80
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DOI: https://doi.org/10.1007/978-3-030-32239-7_80
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