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IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

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Abstract

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet (Li et al (2020)) architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet (Hu et al. (2019) IEEE Access 7:174167-174177) model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.

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Availability of data and material

All datasets are publicly available DRIVE- https://drive.grand-challenge.org/ STARE-https://cecas.clemson.edu/~ahoover/stare/probing/index.html CHASE DB1- https://blogs.kingston.ac.uk/retinal/chasedb1/

Code availability

We are intending to build a larger system. Once all components get published, we will share the code.

References

  1. Abadi M, Barham P, Chen J, et al (2016) TensorFlow: A system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). pp 265–283

  2. Abràmoff MD, Garvin MK, Sonka M (2010) Retinal Imaging and Image Analysis. IEEE Rev Biomed Eng 3:169–208. https://doi.org/10.1109/RBME.2010.2084567

    Article  Google Scholar 

  3. Alom MZ, Hasan M, Yakopcic C, et al (2018) Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv:180206955 [cs]

  4. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19:46–57. https://doi.org/10.1016/j.media.2014.08.002

    Article  Google Scholar 

  5. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

    Article  Google Scholar 

  6. Chanwimaluang T, Fan G (2003) An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In: 2003 IEEE International Symposium on Circuits and Systems (ISCAS). p V–V

  7. Chatziralli IP, Kanonidou ED, Keryttopoulos P et al (2012) The Value of Fundoscopy in General Practice. Open Ophthalmol J 6:4–5. https://doi.org/10.2174/1874364101206010004

    Article  Google Scholar 

  8. Dai J, He K, Sun J (2015) BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp 1635–1643

  9. Fan Z, Lu J, Wei C et al (2019) A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images. IEEE Trans Image Process 28:2367–2377. https://doi.org/10.1109/TIP.2018.2885495

    Article  MathSciNet  Google Scholar 

  10. Fraz MM, Barman SA, Remagnino P et al (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 108:600–616. https://doi.org/10.1016/j.cmpb.2011.08.009

    Article  Google Scholar 

  11. Fraz MM, Remagnino P, Hoppe A et al (2012) An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation. IEEE Trans Biomed Eng 59:2538–2548. https://doi.org/10.1109/TBME.2012.2205687

    Article  Google Scholar 

  12. Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210. https://doi.org/10.1109/42.845178

    Article  Google Scholar 

  13. Hu J, Wang H, Gao S et al (2019) S-UNet: A Bridge-Style U-Net Framework With a Saliency Mechanism for Retinal Vessel Segmentation. IEEE Access 7:174167–174177. https://doi.org/10.1109/ACCESS.2019.2940476

    Article  Google Scholar 

  14. Jin Q, Meng Z, Pham TD et al (2019) DUNet: A deformable network for retinal vessel segmentation. Knowl-Based Syst 178:149–162. https://doi.org/10.1016/j.knosys.2019.04.025

    Article  Google Scholar 

  15. Kingma DP, Ba J (2017) Adam: A Method for Stochastic Optimization. arXiv:14126980 [cs]

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems. Curran Associates, Inc.

  17. Lam BSY, Gao Y, Liew AW-C (2010) General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling. IEEE Trans Med Imaging 29:1369–1381. https://doi.org/10.1109/TMI.2010.2043259

    Article  Google Scholar 

  18. LeCun Y, Boser B, Denker J, et al (1989) Handwritten Digit Recognition with a Back-Propagation Network. In: Advances in Neural Information Processing Systems. Morgan-Kaufmann

  19. Li L, Verma M, Nakashima Y, et al (2020) IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). pp 3645–3654

  20. Li Q, Feng B, Xie L et al (2016) A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images. IEEE Trans Med Imaging 35:109–118. https://doi.org/10.1109/TMI.2015.2457891

    Article  Google Scholar 

  21. Liskowski P, Krawiec K (2016) Segmenting Retinal Blood Vessels With Deep Neural Networks. IEEE Trans Med Imaging 35:2369–2380. https://doi.org/10.1109/TMI.2016.2546227

    Article  Google Scholar 

  22. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 3431–3440

  23. Marín D, Aquino A, Gegundez-Arias ME, Bravo JM (2011) A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Trans Med Imaging 30:146–158. https://doi.org/10.1109/TMI.2010.2064333

    Article  Google Scholar 

  24. Martinez-Perez ME, Hughes AD, Thom SA et al (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11:47–61. https://doi.org/10.1016/j.media.2006.11.004

    Article  Google Scholar 

  25. Mou L, Zhao Y, Fu H, et al (2021) CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Medical Image Analysis 67:101874. https://doi.org/10.1016/j.media.2020.101874

  26. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omnipress, Madison, WI, USA, pp 807–814

  27. Oliveira A, Pereira S, Silva CA (2018) Retinal vessel segmentation based on Fully Convolutional Neural Networks. Expert Syst Appl 112:229–242. https://doi.org/10.1016/j.eswa.2018.06.034

    Article  Google Scholar 

  28. Owen CG, Rudnicka AR, Mullen R et al (2009) Measuring Retinal Vessel Tortuosity in 10-Year-Old Children: Validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) Program. Invest Ophthalmol Vis Sci 50:2004–2010. https://doi.org/10.1167/iovs.08-3018

    Article  Google Scholar 

  29. Patton N, Aslam TM, MacGillivray T et al (2006) Retinal image analysis: Concepts, applications and potential. Prog Retin Eye Res 25:99–127. https://doi.org/10.1016/j.preteyeres.2005.07.001

    Article  Google Scholar 

  30. Pizer SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39:355–368. https://doi.org/10.1016/S0734-189X(87)80186-X

    Article  Google Scholar 

  31. Ricci E, Perfetti R (2007) Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans Med Imaging 26:1357–1365. https://doi.org/10.1109/TMI.2007.898551

    Article  Google Scholar 

  32. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241

  33. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification. IEEE J Biomed Health Inform 19:1118–1128. https://doi.org/10.1109/JBHI.2014.2335617

    Article  Google Scholar 

  34. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative Vessel Segmentation of Fundus Images. IEEE Trans Biomed Eng 62:1738–1749. https://doi.org/10.1109/TBME.2015.2403295

    Article  Google Scholar 

  35. Sasaki Y (2007) The truth of the F-measure. Teach Tutor Mater

  36. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  MATH  Google Scholar 

  37. Staal J, Abramoff MD, Niemeijer M et al (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509. https://doi.org/10.1109/TMI.2004.825627

    Article  Google Scholar 

  38. Tian F, Li Y, Wang J, Chen W (2021) Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology. Computational and Mathematical Methods in Medicine 2021:e4761517. https://doi.org/10.1155/2021/4761517

  39. Uysal E, Güraksin GE (2021) Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks. Multimed Tools Appl 80:3505–3528. https://doi.org/10.1007/s11042-020-09372-w

    Article  Google Scholar 

  40. Yan Z, Yang X, Cheng K-T (2018) Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation. IEEE Trans Biomed Eng 65:1912–1923. https://doi.org/10.1109/TBME.2018.2828137

    Article  Google Scholar 

  41. Yan Z, Yang X, Cheng K-T (2019) A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation. IEEE J Biomed Health Inform 23:1427–1436. https://doi.org/10.1109/JBHI.2018.2872813

    Article  Google Scholar 

  42. Zhang Y, He M, Chen Z, et al (2022) Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation. Expert Systems with Applications 195:116526. https://doi.org/10.1016/j.eswa.2022.116526

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Ashish Kumar & R. K. Agrawal

  2. All India Institute of Medical Sciences, New Delhi, India

    Leve Joseph

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  1. Ashish Kumar
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  2. R. K. Agrawal
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  3. Leve Joseph
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Correspondence to Ashish Kumar.

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Kumar, A., Agrawal, R.K. & Joseph, L. IterMiUnet: A lightweight architecture for automatic blood vessel segmentation. Multimed Tools Appl 82, 43207–43231 (2023). https://doi.org/10.1007/s11042-023-15433-7

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