ABSTRACT
Diabetic Retinopathy (DR) is a fast-progressing disease affecting millions of people world-wide. An early diagnosis is very important to prevent further damage, which can be done by analysis of the Eye Fundus Images (EFI). In this context, deep learning networks can be used to help medical doctors, both by segmenting potential lesions automatically and by classifying the degree of the illness at a certain instant in time. The segmentation task classifies each individual pixel as belonging to either background (BK=non-lesion), microaneurism (MA), soft or hard exudate (SE and HE) or hemorrhage (HM), the optic disk (OD) and the macula (M). Existing deep learning-based segmentation approaches can detect lesions, but there are a relevant number of pixel misclassifications that should be dealt with. Besides calling attention to the issue of correctly evaluating lesions segmentation quality by using the most appropriate metrics, in this paper we investigate the possibility of bringing together the output labelmaps of different deep learning networks and also hardcoded segmentation to improve the end result by means of filtering/merging/voting. Using a publicly available dataset, we show that the approach improves quality significantly as measured using Intersection over the Union IoU (or Jaccard Index JI), from initial IoU scores of 0.9 (BK) 0.09 (MA) 0.17 (HM) 0.29 (HE) 0.18 (SE) 0.8 (OD) to a final score of 0.99 (BK) 0.143 (MA) 0.32 (HM) 0.39 (HE) 0.37 (SE) 0.9 (OD). This corresponds to a significant improvement of around plus 10 percentage points in average. We end the work by delineating future work on this promising direction of research.
- Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data, 3(3), 25.Google ScholarCross Ref
- Deng, Jia, "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009.Google Scholar
- Köse, Cemal, "Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images." Computer methods and programs in biomedicine 107.2 (2012): 274-293.Google Scholar
- Qureshi, I., Ma, J., & Abbas, Q. (2019). Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry, 11(6), 749.Google ScholarCross Ref
- Asiri, N., Hussain, M., Al Adel, F., & Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial intelligence in medicine.Google ScholarDigital Library
- Raman, R., Srinivasan, S., Virmani, S., Sivaprasad, S., Rao, C., & Rajalakshmi, R. (2019). Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye, 33(1), 97-109.Google ScholarCross Ref
- Prentašić, P., & Lončarić, S. (2015, September). Detection of exudates in fundus photographs using convolutional neural networks. In 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 188-192).Google Scholar
- Gondal, W. M., Köhler, J. M., Grzeszick, R., Fink, G. A., & Hirsch, M. (2017, September). Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In 2017 IEEE international conference on image processing (ICIP) (pp. 2069-2073).Google ScholarDigital Library
- Quellec, G., Charrière, K., Boudi, Y., Cochener, B., & Lamard, M. (2017). Deep image mining for diabetic retinopathy screening. Medical image analysis, 39, 178-193.Google Scholar
- Haloi, M. (2015). Improved microaneurysm detection using deep neural networks. arXiv preprint arXiv:1505.04424.Google Scholar
- Van Grinsven, M. J., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging, 35(5), 1273-1284.Google Scholar
- Orlando, J. I., Prokofyeva, E., del Fresno, M., & Blaschko, M. B. (2018). An ensemble deep learning based approach for red lesion detection in fundus images. Computer methods and programs in biomedicine, 153, 115-127.Google Scholar
- Shan, J., & Li, L. (2016, June). A deep learning method for microaneurysm detection in fundus images. In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 357-358).Google ScholarCross Ref
- Kälviäinen, R. V. J. P. H., & Uusitalo, H. (2007). DIARETDB1 diabetic retinopathy database and evaluation protocol. In Medical Image Understanding and Analysis (Vol. 2007, p. 61).Google Scholar
- Erginay, A., Chabouis, A., Viens-Bitker, C., Robert, N., Lecleire-Collet, A., Massin, P., Jun 2008. OPHDIAT: quality-assurance programme plan and performance of the network. Diabetes Metab 34 (3), 235–42.Google ScholarCross Ref
- E. Tiu, “Metrics to evaluate your semantic segmentation model.,” [URL Ac- cessed 8/2019]. URL: https://towardsdatascience.com /metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2 (2019).Google Scholar
- D. L. Csurka, G. and F. Perronnin, “What is a good evaluation measure for semantic segmentation?,” Proceedings of the British Machine Vision Conference , 32.1–32.11. (2013).Google ScholarCross Ref
- Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).Google ScholarCross Ref
- Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.Google Scholar
Index Terms
- Merging/Filtering/Voting to Improve Segmentation of Diabetic Retinopathy Eye Fundus Lesions
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