Wan Cheng, Wu Jiasheng, Li Han, Yan Zhipeng, Wang Chenghu, Jiang Qin, Cao Guofan, Xu Yanwu, Yang Weihua
College of Electronic and Information Engineering/College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Front Neurosci. 2021 Oct 13;15:758887. doi: 10.3389/fnins.2021.758887. eCollection 2021.
In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).
近年来，中国近视人数不断增加，尤其是年轻一代。普通近视可能会发展为高度近视。高度近视会导致视力损害甚至失明。视乳头旁萎缩（PPA）是一种与高度近视相关的典型视网膜病变，也是诊断高度近视的一个基本线索。因此，PPA的准确分割对于高度近视的诊断和治疗至关重要。在本研究中，我们提出了一种优化的Unet（OT-Unet）来解决这一重要任务。OT-Unet使用预训练模型之一：视觉几何组（VGG）、残差网络（ResNet）和残差2网络（Res2Net）作为骨干，并结合边缘注意力、并行部分解码器和反向注意力模块来提高分割精度。一般来说，使用预训练模型可以用更少的样本提高准确率。边缘注意力模块提取轮廓信息，并行部分解码器模块结合多尺度特征，反向注意力模块整合高低层特征。我们还提出了一种增强损失函数，增加复杂像素的权重，使网络能够分割更复杂的病变区域。基于一个包含360张图像的数据集（包括PALM提供的26张图片），所提出的OT-Unet实现了0.9235的高曲线下面积（AUC），表明比原始Unet（0.7917）有显著提高。
