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[Regional classification-guided wavelet Y-Net network for hard exudate segmentation in fundus images]
1School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.
Objective:
We propose an hard exudate(EX)segmentation algorithm based on regional classification-guided wavelet Y-Net network to eliminate the influence of optic disc on EX segmentation process.
Methods:
The wavelet Y-Net network was an end-to-end fundus image EX segmentation network, which combined the regional detection of optic disc and hard exudates segmentation by regional classification-guided EX segmentation to effectively reduce the interference of optic disc in EX segmentation.To avoid failure of small EX region segmentation caused by information loss due to down-sampling operation, discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) were introduced to replace the traditional pooling down-sampling and up-sampling operations.Meanwhile, the inception module based on residual connection was used to obtain the multi-scale features.The proposed algorithm was trained and tested on the IDRiD and e-ophtha EX datasets and evaluated at the pixel level.
Results:
For IDRiD and e-ophtha EX datasets, the proposed algorithm achieved accuracy rates of 0.9858 and 0.9938 with AUC values of 0.9880 and 0.9986, respectively.
Conclusion:
The proposed method can effectively avoid the influence of the optic disc, retain the image details, and improve the effect of EX segmentation.
