EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection
Abstract
:1. Introduction
1.1. Background
1.1.1. Existing Segmentation Methods
1.1.2. Generative Spatial Generative Model
2. Materials and Methods
2.1. Our Framework
2.2. Segmentation of Cup and Disc via EffUnet
2.3. Classification of Images via SpaGen
2.4. Experiments
3. Results
3.1. Segmentation Model: Computational Complexity and Accuracy
3.2. Segmentation Model: Reliability of Vertical CDR
3.3. EffUnet-SpaGen: Reliability of RDS
3.4. EffUnet-SpaGen: Internal Validation for Glaucoma Detection in ORIGA and DRISHTI Datasets
3.5. Comparison Results of Our Method for ORIGA Dataset
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Methods | JC | DC | Acc | Number of Parameters | Ratio of Parameters | Training Time (Minutes) |
---|---|---|---|---|---|---|
ResNet34-Unet [7] | 0.845 | 0.910 | 0.9966 | 24,456,444 | 1.93 | 55 |
ResNet18-Unet | 0.846 | 0.911 | 0.9967 | 14,340,860 | 1.134 | 49 |
EffUnet (our method) | 0.854 | 0.916 | 0.9968 | 12,641,459 | 1 | 42 |
Author | Method | Optic Disc | Dataset | ||
---|---|---|---|---|---|
DC | JC | Acc | |||
Wong et al. [34] | Support vector machine-based classification mechanism | - | 0.940 | 0.990 | SiMES |
Yu et al. [30] | Directional matched filtering and level sets | - | 0.844 | - | MESSIDOR |
Mookiah et al. [35] | Attanassov intuitionistic fuzzy histon (A-IFSH) based method | 0.920 | - | 0.934 | Private |
Giachetti et al. [36] | Iteratively refined model based on contour search constrained by vessel density | - | 0.861 | - | MESSIDOR |
Dashtbozorg et al. [37] | Sliding band filter | - | 0.890 | - | MESSIDOR |
- | 0.850 | - | INSPIRE-AVR | ||
Basit and Fraz [38] | Morphological operations, smoothing filters, 3* and the marker controlled watershed transform | - | 0.710 | - | Shifa |
- | 0.456 | - | 3*CHASE-DB1 | ||
- | 0.547 | - | 3*DIARETDB1 | ||
- | 0.619 | - | DRIVE | ||
Wang et al. [39] | Level set method | - | 0.882 | - | DRIVE |
- | 0.882 | - | DIARETDB1 | ||
- | 0.891 | - | DIARETDB0 | ||
Hamednejad et al. [40] | DBSCAN clustering algorithm | - | - | 0.782 | DRIVE |
Roychowdhury et al. [41] | Region-based features and supervised classification | - | 0.807 | 0.991 | DRIVE |
- | 0.802 | 0.996 | DIARETDB1 | ||
- | 0.776 | 0.996 | DIARETDB0 | ||
- | 0.808 | 0.991 | CHASE-DB1 | ||
- | 0.837 | 0.996 | MESSIDOR | ||
- | 0.729 | 0.985 | STARE | ||
Girard et al. [42] | Local K-means clustering | - | 0.900 | - | MESSIDOR |
Akyol et al. [43] | Keypoint detection, texture analysis, and visual dictionary | - | - | 0.944 | DIARETDB1 |
- | - | 0.950 | DRIVE | ||
- | - | 0.900 | ROC | ||
Abdullah et al. [44] | Circular Hough transform and grow-cut algorithm | - | 0.786 | - | DRIVE |
- | 0.851 | - | DIARETDB1 | ||
- | 0.832 | - | CHASE-DB1 | ||
- | 0.879 | - | MESSIDOR | ||
- | 0.861 | - | Private | ||
Tan et al. [45] | 7-Layer CNN | - | - | - | DRIVE |
Zahoor et al. [46] | Polar transform | - | 0.874 | - | DIARETDB1 |
- | 0.844 | - | MESSIDOR | ||
- | 0.756 | - | DRIVE | ||
Sigut et al. [47] | Contrast based circular approximation | - | 0.890 | - | MESSIDOR |
Noor et al. [31] | Colour multi-thresholding segmentation | 0.590 | - | 0.709 | DRIVE |
Khalid et al. [32] | Fuzzy c-Means (FCM) and morphological operations | - | - | 0.937 | DRIVE |
Yin et al. [48] | Statistical model | - | 0.920 | - | ORIGA |
Fu et al. [14] | Multi-label deep learning and Polar transformation (DL) | - | 0.929 | - | ORIGA |
Al-Bander et al. [33] | Fully convolutional DenseNet | 0.965 | 0.933 | 0.999 | ORIGA |
Proposed method | EffUnet | 0.999 | 0.998 | 0.999 | ORIGA |
Author | Method | Optic Cup | Dataset | ||
---|---|---|---|---|---|
DC | JC | Acc | |||
Hatanaka et al. [49] | Detection of blood vessel bends and features determined from the density gradient | - | - | - | Private |
Almazroa et al. [50] | Thresholding using type-II Fuzzy method | - | - | 0.761 | BinRushed |
- | - | 0.724 | Magrabi | ||
- | - | 0.815 | MESSIDOR | ||
Noor et al. [31] | Colour multi-thresholding segmentation | 0.510 | - | 0.673 | DRIVE |
Khalid et al. [32] | Fuzzy c-Means (FCM) and morphological operations | - | - | 0.903 | DRIVE |
Yin et al. [51] | Sector-based and intensity with shape constraints | 0.830 | - | - | ORIGA |
Yin et al. [48] | Statistical model | 0.810 | - | - | ORIGA |
Xu et al. [52] | Low-rank superpixel representation | - | 0.744 | - | ORIGA |
Tan et al. [53] | Multi-scale superpixel classification | - | 0.752 | - | ORIGA |
Fu et al. [14] | Multi-label deep learning and Polar transformation | - | 0.770 | - | ORIGA |
Al-Bander et al. [33] | Fully convolutional DenseNet | 0.866 | 0.769 | 0.999 | ORIGA |
Proposed method | EffUnet | 0.870 | 0.782 | 0.998 | ORIGA |
Author | Optic Disc | Optic Cup | ||
---|---|---|---|---|
DC | JC | DC | JC | |
Sevastopolsky [54] | - | - | 0.850 | 0.750 |
Zilly et al. [55] | 0.973 | 0.914 | 0.871 | 0.850 |
Al-Bander et al. [33] | 0.949 | 0.904 | 0.828 | 0.711 |
Shuang et al. [7] | 0.974 | 0.949 | 0.888 | 0.804 |
Proposed method | 0.999 | 0.998 | 0.923 | 0.861 |
Segmentation Model | Generative Model (n of Parameters) | Results for ORIGA (Top), DRISHTI (Bottom) | ||||
---|---|---|---|---|---|---|
AUROC | Sen | Spe | PPV | NPV | ||
EffUnet | CDAR (2) | 0.844 | 0.847 | 0.726 | 0.882 | 0.663 |
0.856 | 0.737 | 0.923 | 0.966 | 0.545 | ||
EffUnet | CDR profile of 24values and 1 variance parameter (13) | 0.939 | 0.842 | 0.921 | 0.816 | 0.934 |
0.879 | 0.789 | 0.923 | 0.968 | 0.600 | ||
EffUnet | CDR profile of 24 values and 2 variance parameters (14) | 0.965 | 0.863 | 0.961 | 0.901 | 0.944 |
0.933 | 0.895 | 0.923 | 0.971 | 0.750 | ||
EffUnet | CDR profile of 24 values and 1 variance parameters and CDAR (14) | 0.994 | 0.979 | 0.961 | 0.912 | 0.991 |
0.923 | 0.842 | 0.923 | 0.970 | 0.667 | ||
EffUnet | CDR profile of 24 values and 2 variance parameters and CDAR (15) | 0.997 | 0.989 | 0.974 | 0.940 | 0.996 |
0.969 | 0.947 | 0.923 | 0.973 | 0.857 |
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Krishna Adithya, V.; Williams, B.M.; Czanner, S.; Kavitha, S.; Friedman, D.S.; Willoughby, C.E.; Venkatesh, R.; Czanner, G. EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection. J. Imaging 2021, 7, 92. https://doi.org/10.3390/jimaging7060092
Krishna Adithya V, Williams BM, Czanner S, Kavitha S, Friedman DS, Willoughby CE, Venkatesh R, Czanner G. EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection. Journal of Imaging. 2021; 7(6):92. https://doi.org/10.3390/jimaging7060092
Chicago/Turabian StyleKrishna Adithya, Venkatesh, Bryan M. Williams, Silvester Czanner, Srinivasan Kavitha, David S. Friedman, Colin E. Willoughby, Rengaraj Venkatesh, and Gabriela Czanner. 2021. "EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection" Journal of Imaging 7, no. 6: 92. https://doi.org/10.3390/jimaging7060092