Structured layer surface segmentation for retina OCT using fully convolutional regression networks.
Structured layer surface segmentation for retina OCT using fully convolutional regression networks.
复制标题
使用完全卷积回归网络的视网膜OCT的结构化层表面分割。
DOI：
10.1016/j.media.2020.101856
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发表时间：
2021-03
影响因子：
10.9
通讯作者：
Prince JL
中科院分区：
文献类型：
--
作者：
He Y;Carass A;Liu Y;Jedynak BM;Solomon SD;Saidha S;Calabresi PA;Prince JL
光学相干断层扫描（OCT）是一种具有微米分辨率的非侵入性成像方式，已广泛用于扫描视网膜。视网膜层是许多疾病的重要生物标志物。用于分割具有正确层次结构（拓扑）的平滑连续层表面的准确自动化算法对于自动化视网膜厚度和表面形状分析非常重要。最先进的方法通常使用两步过程。首先，使用经过训练的分类器将每个像素标记为背景和图层或边界和非边界。其次，通过图形方法（例如图形切割）提取具有正确拓扑的所需光滑表面。像深度网络这样的数据驱动方法在像素分类步骤中表现出了强大的能力，但迄今为止还无法在第二步中提取具有拓扑约束的结构化平滑连续表面。在本文中，我们通过直接建模表面位置的分布，将这两个步骤结合到一个统一的深度学习框架中。通过单次前馈操作即可获得平滑、连续且拓扑正确的表面。所提出的方法在健康对照和患有多发性硬化症或糖尿病性黄斑水肿的受试者的两个公开数据集上进行了评估，并被证明能够以亚像素精度实现最先进的性能。
Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.
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影响因子：
10.9
作者：
Lang A;Carass A;Jedynak BM;Solomon SD;Calabresi PA;Prince JL
通讯作者：
Prince JL
影响因子：
4.4
作者：
Girard, Michael J. A.;Strouthidis, Nicholas G.;Mari, Jean Martial
通讯作者：
Mari, Jean Martial
影响因子：
3.4
作者：
Carass, Aaron;Lang, Andrew;Prince, Jerry L.
通讯作者：
Prince, Jerry L.
影响因子：
1.2
作者：
He, Yufan;Carass, Aaron;Prince, Jerry L.
通讯作者：
Prince, Jerry L.
影响因子：
3.4
作者：
Karri, S. P. K.;Chakraborthi, Debjani;Chatterjee, Jyotirmoy
通讯作者：
Chatterjee, Jyotirmoy