Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years. Accurate segmentation of biomarkers such as drusen that points to the early stages of AMD is crucial in preventing further vision impairment. However, segmenting drusen is extremely challenging due to their varied sizes and appearances, low contrast and noise resemblance. Most existing literature, therefore, have focused on size estimation of drusen using classification, leaving the challenge of accurate segmentation less tackled. Additionally, obtaining the pixel-wise annotations is extremely costly and such labels can often be noisy, suffering from inter-observer and intra-observer variability. Quantification of uncertainty associated with segmentation tasks offers principled measures to inspect the segmentation output. Realizing its utility in identifying erroneous segmentation and the potential applications in clinical decision making, here we develop a U-Net based drusen segmentation model and quantify the segmentation uncertainty. We investigate epistemic uncertainty capturing the model confidence and aleatoric uncertainty capturing the data uncertainty. We present segmentation results and show how uncertainty can help formulate robust evaluation strategies. We visually inspect the pixel-wise uncertainty and segmentation results on test images. We finally analyze the correlation between segmentation uncertainty and accuracy. Our results demonstrate the utility of leveraging uncertainties in developing and explaining segmentation models for medical image analysis.

本研究通过开发基于U-Net的drusen分割模型和量化分割不确定性，探究了利用不确定性进行精确定位的可行性并分析了分割准确度与不确定性之间的相关性，从而证明了在医学图像分析中利用不确定性来开发和解释分割模型的有效性。

光学相干断层扫描图像中Drusen分割的认识和偶然不确定性分析