RV-GAN : Retinal Vessel Segmentation from Fundus Images using Multi-scale Generative Adversarial Networks

• URL: http://arxiv.org/abs/2101.00535v1
• Date: Sun, 3 Jan 2021 01:04:49 GMT
• Title: RV-GAN : Retinal Vessel Segmentation from Fundus Images using Multi-scale Generative Adversarial Networks
• Authors: Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod, Kenton M. Sanders, Salah A. Baker
• Abstract summary: RVGAN is a new multi-scale generative architecture for accurate retinal vessel segmentation. Our architecture uses two generators and two multi-scale autoencoder based discriminators, for better microvessel localization and segmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retinal vessel segmentation contributes significantly to the domain of retinal image analysis for the diagnosis of vision-threatening diseases. With existing techniques the generated segmentation result deteriorates when thresholded with higher confidence value. To alleviate from this, we propose RVGAN, a new multi-scale generative architecture for accurate retinal vessel segmentation. Our architecture uses two generators and two multi-scale autoencoder based discriminators, for better microvessel localization and segmentation. By combining reconstruction and weighted feature matching loss, our adversarial training scheme generates highly accurate pixel-wise segmentation of retinal vessels with threshold >= 0.5. The architecture achieves AUC of 0.9887, 0.9814, and 0.9887 on three publicly available datasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally, RV-GAN outperforms other architectures in two additional relevant metrics, Mean-IOU and SSIM.

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