Browse > Article
http://dx.doi.org/10.9708/jksci.2022.27.09.001

Corneal Ulcer Region Detection With Semantic Segmentation Using Deep Learning  

Im, Jinhyuk (Graduate School of Computers, Dankook University)
Kim, Daewon (Department of Computer Engineering, Dankook University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.9, 2022 , pp. 1-12 More about this Journal
Abstract
Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.
Keywords
Semantic Segmentation; Corneal Ulcer; DeepLab; Xception; ResNet; Dice Similarity Coefficient (DSC); IoU;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Yuan, M. Chao and Y. Lo, "Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance," IEEE Trans. on Medical Imaging, Vol. 36, No. 9, pp. 1876-1886, Sep. 2017.   DOI
2 H. Lee, F. M. Troschel, S. Tajmir, G. Fuchs, J. Mario, F. J. Fintelmann and S. Do, "Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis," Journal of digital imaging, Vol. 30, No. 4, pp. 487-498. Jun. 2017.   DOI
3 M. Teichmann, M. Weber, M. Zollner, R. Cipolla and R. Urtasun, "Multinet: Real-time joint semantic reasoning for autonomous driving," 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1013-1020, Changshu, Suzhou, China, Jun. 2018.
4 J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431-3440, 2015.
5 Y. Chu, J. Fei and S. Hou, "Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure," IEEE Trans. on Neural Networks and Learning Systems, Vol. 31, No. 4, pp. 1297-1309, Apr. 2020. DOI: 10.1109/TNNLS.2019.2919676.   DOI
6 D. Wang, D. Zhang, G. Yang, B. Xu, Y. Luo and X. Yang, "SSRNet: In-Field Counting Wheat Ears Using Multi-Stage Convolutional Neural Network," IEEE Trans. on Geoscience and Remote Sensing, Vol. 60, pp. 1-11, 2022. Art No. 4403311, DOI: 10.1109/TGRS.2021.3093041.   DOI
7 H. Noh, S. Hong, and B. Han, "Learning Deconvolution Network for Semantic Segmentation," The IEEE Conference on Computer Vision (ICCV), pp. 1520-1528, 2015.
8 Y. Tsai, W. C. Hung, S. Schulter and K. Sohn, "Learning to adapt structured output space for semantic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472-7481, Feb. 2018.
9 Z. Wang, and S. Sarcar, "Outline Objects using Deep Reinforcement Learning," arXiv preprint arXiv : 1804.04603, Apr. 2018.
10 S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang and P. H. S. Torr, "Conditional random fields as recurrent neural networks," Proceedings of the IEEE international conference on computer vision, pp. 1529-1537, 2015.
11 V. Iglovikov, S. Mushinskiy and V. Osin, "Satellite imagery feature detection using deep convolutional neural network: A kaggle competition," arXiv preprint arXiv:1706.06169, Jun 2017.
12 Y. Ma, J. Liu, Y. Liu, H. Fu, Y. Hu, J. Cheng, H. Qi, Y. Wu, J. Zhang, and Y. Zhao, "Structure and Illumination Constrained GAN for Medical Image Enhancement," IEEE Trans. on Medical Imaging, Vol. 40, No. 12, pp. 3955-3967, Dec. 2021. DOI: 10.1109/TMI.2021.3101937.   DOI
13 X. Du, M. El-Khamy, J. Lee and L. Davis, "Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection," 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 953-961, March 2017.
14 L. Tai, G. Paolo and M. Liu, "Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 31-36, Sep. 2017.
15 G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S.Ourselin and T. Vercauteren, "Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning," IEEE Trans. on Medical Imaging, Vol. 37, No. 7, pp. 1562-1573, Jul. 2018.   DOI
16 D. Lin, J. Dai, J. Jia, K. He and J. Sun, "ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3159-3167, Apr. 2016.
17 N. A. T. Otoum, "Medical Iamge Processing : applications in ophthalmology and total hip replacement," Loughborough University, pp. 52-74, 2012.
18 T. F. Chen, and L. A. Vese, "Active Contours Without Edges," IEEE Trans. on Image Processing, Vol. 10, No. 2, pp. 266-277, Feb. 2001.   DOI
19 T. P. Patel, N. V. Prajna, S. Farsiu, N. G. Valikodath, L. M. Niziol, L. Dudeja, K. H. Kim and M. A. Woodward, "Novel Image Based Analysis for Reduction of Clinician-Dependent Varability in Measurement of the Corneal Ulcer Size," Clinical Science "Cornea", pp. 331-339, Mar. 2018.
20 Q. Sun, L. Deng, J. Liu, H. Huang, J. Yuan and X. Tang, "Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation," Fetal, Infant and Ophthalmic Medical Image Analysis, Springer, Cham, pp. 101-108, 2017.
21 L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, "Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs," arXiv preprint arXiv:1412.7062, 2014.
22 C. Chen, J. H. Chuah, R. Ali and Y. Wang, "Retinal Vessel Segmentation Using Deep Learning: A Review," IEEE Access, Vol. 9, pp. 111985-112004, 2021. DOI: 10.1109/AC CESS.2021.3102176.   DOI
23 H. Fu, Y. Xu, D. W. K. Wong and J. Liu, "Retinal Vessel Segmentation via Deep Learning Network And Fully-connected Conditional Random Fields," 2016 IEEE 13th International Symposium on Biomedical Imaging(ISBI), pp. 698-701, Apr. 2016.
24 G. Lenczner, A. Chan-Hon-Tong, B. Le Saux, N. Luminari and G. Le Besnerais, "DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15, pp. 3376-3389, 2022. DOI: 10.1109/JSTARS.2022.3166551.   DOI
25 C. Peng, K. Zhang, Y. Ma and J. Ma, "Cross Fusion Net: A Fast Semantic Segmentation Network for Small-Scale Semantic Information Capturing in Aerial Scenes," IEEE Trans. on Geoscience and Remote Sensing, Vol. 60, pp. 1-13, 2022. Art no. 5601313, DOI: 10.1109/TGRS.2021.305 3062.   DOI
26 F. Yu, and V. Koltun, "Multi-Scale Context Aggregation By Dilated Convolutions," IEEE Trans. on Parallel and Distributed Systems, Vol. 16, No. 3, pp. 219-232, Mar. 2005.   DOI
27 Y. -J. Ma, H. -H. Shuai and W. -H. Cheng, "Spatiotemporal Dilated Convolution With Uncertain Matching for Video-Based Crowd Estimation," IEEE Trans. on Multimedia, Vol. 24, pp. 261-273, 2022. DOI: 10.1109/TMM.2021.3050059.   DOI