Exploiting multi-granularity visual features for retinal layer segmentation in human eyes - He, Xiang; Wang, Yiming; Poiesi, Fabio; Song, Weiye; Xu, Quanqing; Feng, Zixuan; Wan, Yi (Frontiers in Bioengineering and Biotechnology, 2023)
Accurate segmentation of retinal layer boundaries can facilitate the detection of patients with early ophthalmic disease. Typical segmentation algorithms operate at low resolutions without fully exploiting multi-granularity visual features. Moreover, several related studies do not release their datasets that are key for the research on deep learning-based solutions. We propose a novel end-to-end retinal layer segmentation network based on ConvNeXt, which can retain more feature map details by using a new depth efficient attention module and multi-scale structures. In addition, we provide a semantic segmentation dataset containing 206 retinal images of healthy human eyes (named NR206 dataset), which is easy to use as it does not require any additional transcoding processing. We experimentally show that our segmentation approach outperforms state-of-the-art approaches on this new dataset, achieving the average dice score of 91.3% and mIoU of 84.4%. Our approach also achieves state-of-the-art performance on a glaucoma dataset and a diabetic macular edema (DME) dataset, proving that our model is suitable for other applications. We will make our model and the NR206 dataset publicly available upon acceptance of this paper.