论文标题
以数据为中心的AI方法，以改善OCT EN面对图像中的视神经头部分割和本地化
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
论文作者
论文摘要
视网膜成像数据中解剖特征的自动检测和定位与许多方面有关。在这项工作中，我们遵循以数据为中心的方法，以优化分类器训练，以在光学相干断层扫描中的视神经头部检测和本地化视网膜图像。我们研究了域知识驱动的空间复杂性降低对所得视神经头部分割和定位性能的影响。我们提出了一种机器学习方法，用于在2D范围内进行分段视神经头的3D广场扫描光源光学相干断层扫描扫描，从而可以自动评估大量数据。对手动注释的2D EN面对视网膜图像的评估表明，当基础像素级分类任务通过域知识在空间上放松时，标准U-NET的训练可以改善视神经头部分割和定位性能。
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical coherence tomography scans that enables the automated assessment of large amounts of data. Evaluation on manually annotated 2D en face images of the retina demonstrates that training of a standard U-Net can yield improved optic nerve head segmentation and localization performance when the underlying pixel-level binary classification task is spatially relaxed through domain knowledge.
