Xiong Hao, Liu Sidong, Sharan Roneel V, Coiera Enrico, Berkovsky Shlomo
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Artif Intell Med. 2022 Apr;126:102261. doi: 10.1016/j.artmed.2022.102261. Epub 2022 Feb 26.
Fundus images have been widely used in routine examinations of ophthalmic diseases. For some diseases, the pathological changes mainly occur around the optic disc area; therefore, detection and segmentation of the optic disc are critical pre-processing steps in fundus image analysis. Current machine learning based optic disc segmentation methods typically require manual segmentation of the optic disc for the supervised training. However, it is time consuming to annotate pixel-level optic disc masks and inevitably induces inter-subject variance. To address these limitations, we propose a weak label based Bayesian U-Net exploiting Hough transform based annotations to segment optic discs in fundus images. To achieve this, we build a probabilistic graphical model and explore a Bayesian approach with the state-of-the-art U-Net framework. To optimize the model, the expectation-maximization algorithm is used to estimate the optic disc mask and update the weights of the Bayesian U-Net, alternately. Our evaluation demonstrates strong performance of the proposed method compared to both fully- and weakly-supervised baselines.
眼底图像已广泛应用于眼科疾病的常规检查。对于某些疾病，病理变化主要发生在视盘区域周围；因此，视盘的检测和分割是眼底图像分析中的关键预处理步骤。当前基于机器学习的视盘分割方法通常需要对视盘进行手动分割以进行监督训练。然而，对视盘像素级掩码进行标注很耗时，并且不可避免地会导致个体间差异。为了解决这些限制，我们提出了一种基于弱标签的贝叶斯U-Net，利用基于霍夫变换的标注来分割眼底图像中的视盘。为此，我们构建了一个概率图模型，并探索了一种结合最先进的U-Net框架的贝叶斯方法。为了优化模型，使用期望最大化算法交替估计视盘掩码并更新贝叶斯U-Net的权重。我们的评估表明，与全监督和弱监督基线相比，所提出的方法具有很强的性能。
