Using CNNs for Designing and Implementing an Automatic Vascular Segmentation Method of Biomedical Images

  • Pierangela BrunoEmail author
  • Paolo Zaffino
  • Salvatore Scaramuzzino
  • Salvatore De Rosa
  • Ciro Indolfi
  • Francesco Calimeri
  • Maria Francesca Spadea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)


The assessment of vascular complexity in the lower limbs provides important information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. Currently, the evaluation is carried out by visual inspection of cine-angiograms, which is largely operator-dependent. An automatic image analysis could offer a fast and more reliable technique to support physicians with the clinical management of these patients. In this work, we introduce a new method to automatically segment the vascular tree from cine-angiography images, in order to improve the clinical interpretation of the complexity of vascular collaterals in Peripheral Arterial Occlusive Disease (PAOD) patients. The approach is based on: (1) a feature-detection method to convert the video into a static image with lager Field Of View (FOV) and (2) a custom Convolutional Neural Network (CNN) for the segmentation of vascular structure. Experimental evaluations over a set of clinical cases confirm the viability of the approach: accuracy is assessed in terms of area under the ROC curve, where an average value of \(0.988 \pm 0.006\) is measured.


Segmentation Convolutional Neural Networks Feature detection Cine-angiography Bioinformatics Biomedical imaging 



The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs that were used in this research.


  1. 1.
    Prior, B.M., et al.: Time course of changes in collateral blood flow and isolated vessel size and gene expression after femoral artery occlusion in rats. Am. J. Physiol.-Hear. Circ. Physiol. 287(6), H2434–H2447 (2004)CrossRefGoogle Scholar
  2. 2.
    McDermott, M.M., et al.: Superficial femoral artery plaque and functional performance in peripheral arterial disease: walking and leg circulation study (WALCS III). JACC: Cardiovasc. Imaging 4(7), 730–739 (2011)Google Scholar
  3. 3.
    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). Scholar
  4. 4.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). Scholar
  5. 5.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)CrossRefGoogle Scholar
  6. 6.
    Canny J.: A computational approach to edge detection. In: Readings in Computer Vision, pp. 184–203 (1987)Google Scholar
  7. 7.
    Yang, S., Yang, J., Wang, Y., Yang, Q., Ai, D., Wang, Y.: Automatic coronary artery segmentation in X-ray angiograms by multiple convolutional neural networks. In: Proceedings of the 3rd International Conference on Multimedia and Image Processing, pp. 31–35 (2018)Google Scholar
  8. 8.
    Moccia, S., De Momi, E., El Hadji, S., Mattos, L.S.: Blood vessel segmentation algorithms - review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158, 71–91 (2018)CrossRefGoogle Scholar
  9. 9.
    Hu, K., et al.: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309, 179–191 (2018)CrossRefGoogle Scholar
  10. 10.
    Alonso-Caneiro, D., Read, S.A., Hamwood, J., Vincent, S.J., Collins, M.J.: Use of convolutional neural networks for the automatic segmentation of total retinal and choroidal thickness in OCT images (2018)Google Scholar
  11. 11.
    Iglovikov, V., Mushinskiy, S., Osin, V.: Satellite imagery feature detection using deep convolutional neural network: a kaggle competition. arXiv preprint arXiv:1706.06169 (2017)
  12. 12.
    Iglovikov, V., Rakhlin, A., Kalinin, A., Shvets, A.: Pediatric bone age assessment using deep convolutional neural networks. arXiv preprint arXiv: 1712.05053 (2017)
  13. 13.
    Hrkac, T., Brkic, K., Kalafatic, Z.: Multi-class U-Net for segmentation of non-biometric identifiersGoogle Scholar
  14. 14.
    Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: IEEE International Conference, pp. 8609–8613 (2013)Google Scholar
  15. 15.
    Gold, S., Rangarajan, A.: Softmax to softassign: neural network algorithms for combinatorial optimization. J. Artif. Neural Netw. 2(4), 381–399 (1996)Google Scholar
  16. 16.
    Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv. 1502.03167 (2015)
  18. 18.
    Kroese, D.P., Rubinstein, R.Y., Cohen, I., Porotsky, S., Taimre, T.: Cross-entropy method. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Operations Research and Management Science. Springer, Boston (2013). Scholar
  19. 19.
    Fenster, A., Chiu, B.: Evaluation of segmentation algorithms for medical imaging. In: Engineering in Medicine and Biology Society, pp. 7186–7189 (2005)Google Scholar
  20. 20.
    Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  21. 21.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  22. 22.
    Marín, D., Aquino, A., Gegúndez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)CrossRefGoogle Scholar
  23. 23.
    Crum, W.R., Camara, O., Hill, D.L.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451–1461 (2006)CrossRefGoogle Scholar
  24. 24.
    Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. OSDI 16, 265–283 (2016)Google Scholar
  25. 25.
    Chollet, F., et al.: Keras (2015)Google Scholar
  26. 26.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
  27. 27.
    Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004: Image Processing, vol. 5370, pp. 648–657 (2004)Google Scholar
  28. 28.
    Fritzsche, K., et al.: Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images. In: State-of-The-Art Angiography, Applications and Plaque Imaging Using MR, CT, Ultrasound and X-rays, pp. 225–298 (2003)Google Scholar
  29. 29.
    Novianto, S., Suzuki, Y., Maeda, J.: Near optimum estimation of local fractal dimension for image segmentation. Pattern Recognit. Lett. 24(1–3), 365–374 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CalabriaRendeItaly
  2. 2.Department of Experimental and Clinical MedicineUniversity of CatanzaroCatanzaroItaly
  3. 3.Division of Cardiology, Department of Medical and Surgical SciencesUniversity of CatanzaroCatanzaroItaly

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