In this paper, we present a new approach for uncertainty-aware retinal layer segmentation in Optical Coherence Tomography (OCT) scans using probabilistic signed distance functions (SDF). Traditional pixel-wise and regression-based methods primarily encounter difficulties in precise segmentation and lack of geometrical grounding respectively. To address these shortcomings, our methodology refines the segmentation by predicting a signed distance function (SDF) that effectively parameterizes the retinal layer shape via level set. We further enhance the framework by integrating probabilistic modeling, applying Gaussian distributions to encapsulate the uncertainty in the shape parameterization. This ensures a robust representation of the retinal layer morphology even in the presence of ambiguous input, imaging noise, and unreliable segmentations. Both quantitative and qualitative evaluations demonstrate superior performance when compared to other methods. Additionally, we conducted experiments on artificially distorted datasets with various noise types-shadowing, blinking, speckle, and motion-common in OCT scans to showcase the effectiveness of our uncertainty estimation. Our findings demonstrate the possibility to obtain reliable segmentation of retinal layers, as well as an initial step towards the characterization of layer integrity, a key biomarker for disease progression. Our code is available at \url{https://github.com/niazoys/RLS_PSDF}.

翻译：本文提出了一种利用概率符号距离函数（SDF）实现光学相干断层扫描（OCT）图像中不确定性感知视网膜层分割的新方法。传统的逐像素分割方法和基于回归的方法分别主要面临分割精度不足和缺乏几何基础的问题。为解决这些缺陷，我们的方法通过预测符号距离函数（SDF）来优化分割，该函数通过水平集有效地参数化视网膜层的形状。我们进一步通过集成概率建模来增强该框架，应用高斯分布来封装形状参数化中的不确定性。这确保了即使在存在模糊输入、成像噪声和不可靠分割的情况下，也能获得鲁棒的视网膜层形态表示。定量和定性评估均表明，与其他方法相比，该方法具有更优的性能。此外，我们在包含OCT扫描中常见各种噪声类型（阴影、眨眼、散斑和运动）的人工失真数据集上进行了实验，以展示我们不确定性估计的有效性。我们的研究结果表明，该方法不仅能够获得可靠的视网膜层分割，而且是实现层完整性表征（疾病进展的关键生物标志物）的初步步骤。我们的代码可在 \url{https://github.com/niazoys/RLS_PSDF} 获取。