Robust Optic Nerve Segmentation on Clinically Acquired CT.
Robust Optic Nerve Segmentation on Clinically Acquired CT.
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DOI：
10.1117/12.2043715
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发表时间：
2014-03-21
期刊：
影响因子：
--
通讯作者：
Landman BA
中科院分区：
文献类型：
--
作者：
Panda S;Asman AJ;Delisi MP;Mawn LA;Galloway RL;Landman BA
视神经是一种敏感的中枢神经系统结构，在许多破坏性的病理条件下起着关键作用。近年来，已经提出了几种方法来自动分割视神经，但朝着完全自动化的方向进展有限。多图谱方法已经成功地用于脑分割，但它们在较小解剖结构中的应用仍然相对未被探索。在这里，我们评估了一个框架的强大和全自动分割的视神经，眼球和肌肉。我们采用了一个强大的配准过程，准确的注册，可变体素分辨率和图像视野。我们证明了SyN注册和最近提出的标签融合算法（非本地空间STAPLE），占小规模的注册对应错误的最佳组合的功效。在包含30个高度变化的人脑计算机断层扫描（CT）图像的数据集上，最佳配准和标签融合流水线导致视神经的中值Dice相似性系数为0.77，对称平均表面距离误差为0.55 mm，对称Hausdorff距离误差为3.33 mm。同时，我们证明了最佳算法的鲁棒性分割的视神经结构在316 CT扫描从甲状腺眼病（TED）患者人群的182名受试者。
The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image field-of-view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.
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影响因子：
10.6
作者：
Warfield, SK;Zou, KH;Wells, WM
通讯作者：
Wells, WM
影响因子：
3.2
作者：
TURK, M;PENTLAND, A
通讯作者：
PENTLAND, A
影响因子：
5.7
作者：
Cardoso, M. Jorge;Clarkson, Matthew J.;Ridgway, Gerard R.;Modat, Marc;Fox, Nick C.;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
影响因子：
4.8
作者：
DICE, LR
通讯作者：
DICE, LR
影响因子：
10.6
作者：
Asman AJ;Landman BA
通讯作者：
Landman BA