Fundus image segmentation on unseen domains is challenging, especially for the over-parameterized deep models trained on the small medical datasets. To address this challenge, we propose a method named Adaptive Feature-fusion Neural Network (AFNN) for glaucoma segmentation on unseen domains, which mainly consists of three modules: domain adaptor, feature-fusion network, and self-supervised multi-task learning. Specifically, the domain adaptor helps the pretrained-model fast adapt from other image domains to the medical fundus image domain. Feature-fusion network and self-supervised multi-task learning for the encoder and decoder are introduced to improve the domain generalization ability. In addition, we also design the weighted-dice-loss to improve model performance on complex optic-cup segmentation tasks. Our proposed method achieves a competitive performance over existing fundus segmentation methods on four public glaucoma datasets.

翻译：在未见领域上的眼底图像分割具有挑战性，尤其是对于在小规模医疗数据集上训练的过参数化深度模型而言。为应对这一挑战，我们提出了一种名为自适应特征融合神经网络（AFNN）的方法，用于未见领域的青光眼分割，该方法主要由三个模块组成：领域适配器、特征融合网络和自监督多任务学习。具体而言，领域适配器帮助预训练模型从其他图像领域快速适应至医学眼底图像领域。引入特征融合网络以及针对编码器和解码器的自监督多任务学习，以提升领域泛化能力。此外，我们还设计了加权Dice损失函数，以改善复杂视杯分割任务中的模型性能。在四个公开的青光眼数据集上，我们提出的方法相较于现有眼底分割方法取得了具有竞争力的性能。