Abstract
Arteriovenous nicking (AVN) is a prominent biomarker in assessing cardiac risk factors such as hypertension, atherosclerosis and coronary artery diseases. The retinal junctions play a vital role in AVN quantification and assessment. The human retinal vasculature is a complex tree structure of blood vessels originating from the optic disc and spans over the retina. As the retinal vasculature is multidimensional in nature, identification of retinal junctions at multiple scales is a challenging task. In this paper, to address this multi-scale junction detection and classification, a novel deep learning approach has been proposed using a region-based convolutional neural network (RCNN). The proposed method detects the accurate location of junctions using the junction proposal network (JPN) and classifies the junctions as cross-overs and bifurcations using the junction classification network (JCN). Compared to peer’s work, the proposed method detects multi-scale and immediate junctions. The method also estimates the junction area, which is another quantitative measurement of AVN. The precision, recall scores are evaluated on two publicly available datasets, DRIVE and IOSTAR. The results obtained are analyzed and compared with peer’s work.
Similar content being viewed by others
References
Abbasi-Sureshjani, S., Smit-Ockeloen, I., Bekkers, E., Dashtbozorg, B., ter Haar Romeny, B.: Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 189–192. IEEE (2016)
Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
Almubarak, H., Bazi, Y., Alajlan, N.: Two-stage mask-rcnn approach for detecting and segmenting the optic nerve head, optic disc, and optic cup in fundus images. Appl. Sci. 10(11), 3833 (2020)
Azzopardi, G., Petkov, N.: Automatic detection of vascular bifurcations in segmented retinal images using trainable cosfire filters. Pattern Recogn. Lett. 34(8), 922–933 (2013)
Baboiu, D.M., Hamarneh, G.: Vascular bifurcation detection in scale-space. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 41–46. IEEE (2012)
Calvo, D., Ortega, M., Penedo, M.G., Rouco, J.: Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images. Comput. Methods Progr. Biomed. 103(1), 28–38 (2011)
Chen, Y., Shi, Y., Cui, Y., Chen, X.: Narrow gap deviation detection in keyhole tig welding using image processing method based on mask-rcnn model. Int. J. Adv. Manuf. Technol. 112(7), 2015–2025 (2021)
Fan, Z., Xia, W., Liu, X., Li, H.: Detection and segmentation of underwater objects from forward-looking sonar based on a modified mask rcnn, pp. 1–9. Signal, Image and Video Processing pp (2021)
Fathi, A., Naghsh-Nilchi, A.R., Mohammadi, F.A.: Automatic vessel network features quantification using local vessel pattern operator. Comput. Biol. Med. 43(5), 587–593 (2013)
Grünberg, K., Jimenez-del Toro, O., Jakab, A., Langs, G., Fernandez, T.S., Winterstein, M., Weber, M.A., Krenn, M.: Annotating medical image data. In: Cloud-Based Benchmarking of Medical Image Analysis, pp. 45–67. Springer, Cham (2017)
Hatanaka, Y., Tachiki, H., Ogohara, K., Muramatsu, C., Okumura, S., Fujita, H.: Artery and vein diameter ratio measurement based on improvement of arteries and veins segmentation on retinal images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1336–1339. IEEE (2016)
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)
Lin, B., Sun, Y., Sanchez, J.E., Qian, X.: Efficient vessel feature detection for endoscopic image analysis. IEEE Trans. Biomed. Eng. 62(4), 1141–1150 (2014)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016)
London, A., Benhar, I., Schwartz, M.: The retina as a window to the brain-from eye research to cns disorders. Nature Rev. Neurol. 9(1), 44 (2013)
Morales, S., Naranjo, V., Angulo, J., Legaz-Aparicio, A.G., Verdú-Monedero, R.: Retinal network characterization through fundus image processing: Significant point identification on vessel centerline. Signal Process.: Image Commun. 59, 50–64 (2017)
Pratt, H., Williams, B.M., Ku, J.Y., Vas, C., McCann, E., Al-Bander, B., Zhao, Y., Coenen, F., Zheng, Y.: Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography. J Imaging 4(1), 4 (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)
Rosenbaum, D., Kachenoura, N., Koch, E., Paques, M., Cluzel, P., Redheuil, A., Girerd, X.: Relationships between retinal arteriole anatomy and aortic geometry and function and peripheral resistance in hypertensives. Hypertens. Res. 39(7), 536–542 (2016)
Shaodan, L., Chen, F., Zhide, C.: A ship target location and mask generation algorithms base on mask rcnn. Int. J. Comput. Intell. Syst. 12(2), 1134–1143 (2019)
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Srinidhi, C.L., Rath, P., Sivaswamy, J.: A vessel keypoint detector for junction classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 882–885. IEEE (2017)
Staal, J., Abràmoff, 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)
Tsai, C.L., Stewart, C.V., Tanenbaum, H.L., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. IEEE Trans. Inf. Technol. Biomed. 8(2), 122–130 (2004)
Uslu, F., Bharath, A.A.: A multi-task network to detect junctions in retinal vasculature. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 92–100. Springer (2018)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big data 3(1), 1–40 (2016)
Wong, T.Y., Klein, R., Couper, D.J., Cooper, L.S., Shahar, E., Hubbard, L.D., Wofford, M.R., Sharrett, A.R.: Retinal microvascular abnormalities and incident stroke: the atherosclerosis risk in communities study. Lancet 358(9288), 1134–1140 (2001)
Zhang, Y., Chu, J., Leng, L., Miao, J.: Mask-refined R-CNN: A network for refining object details in instance segmentation. Sensors 20(4), 1010 (2020)
Zhao, H., Sun, Y., Li, H.: Retinal vascular junction detection and classification via deep neural networks. Comput. Methods Progr. Biomed. 183, 105096 (2020)
Acknowledgements
The authors convey deep gratitude to institute management for their constant encouragement and support in carrying out the research work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pampana, L.K., Rayudu, M.S. Detection and classification of multi-scale retinal junctions using region-based CNN. SIViP 16, 265–272 (2022). https://doi.org/10.1007/s11760-021-01986-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-021-01986-3