Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
Med Image Anal. 2021 Dec;74:102253. doi: 10.1016/j.media.2021.102253. Epub 2021 Sep 24.
Glaucoma is an ocular disease threatening irreversible vision loss. Primary screening of Glaucoma involves computation of optic cup (OC) to optic disc (OD) ratio that is widely accepted metric. Recent deep learning frameworks for OD and OC segmentation have shown promising results and ways to attain remarkable performance. In this paper, we present a novel segmentation network, Nested EfficientNet (NENet) that consists of EfficientNetB4 as an encoder along with a nested network of pre-activated residual blocks, atrous spatial pyramid pooling (ASPP) block and attention gates (AGs). The combination of cross-entropy and dice coefficient (DC) loss is utilized to guide the network for accurate segmentation. Further, a modified patch-based discriminator is designed for use with the NENet to improve the local segmentation details. Three publicly available datasets, REFUGE, Drishti-GS, and RIM-ONE-r3 were utilized to evaluate the performances of the proposed network. In our experiments, NENet outperformed state-of-the-art methods for segmentation of OD and OC. Additionally, we show that NENet has excellent generalizability across camera types and image resolution. The obtained results suggest that the proposed technique has potential to be an important component for an automated Glaucoma screening system.
青光眼是一种威胁视力不可逆转丧失的眼部疾病。青光眼的初步筛查包括计算视杯（OC）与视盘（OD）的比值，这是一种广泛接受的指标。最近的 OD 和 OC 分割深度学习框架已经显示出了有前途的结果和实现卓越性能的方法。在本文中，我们提出了一种新的分割网络，嵌套高效网络（NENet），它由 EfficientNetB4 作为编码器，以及嵌套的预激活残差块、空洞空间金字塔池化（ASPP）块和注意力门（AG）网络组成。交叉熵和迪奇系数（DC）损失的组合被用于指导网络进行准确的分割。此外，我们设计了一种改进的基于补丁的鉴别器与 NENet 一起使用，以提高局部分割细节。我们利用三个公开可用的数据集 REFUGE、Drishti-GS 和 RIM-ONE-r3 来评估所提出的网络的性能。在我们的实验中，NENet 在 OD 和 OC 的分割方面优于最先进的方法。此外，我们还表明，NENet 在相机类型和图像分辨率方面具有出色的泛化能力。所得结果表明，该技术有潜力成为自动化青光眼筛查系统的重要组成部分。
