Sleman Ahmed A, Soliman Ahmed, Elsharkawy Mohamed, Giridharan Guruprasad, Ghazal Mohammed, Sandhu Harpal, Schaal Shlomit, Keynton Robert, Elmaghraby Adel, El-Baz Ayman
Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA.
Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, 59911, UAE.
Med Phys. 2021 Apr;48(4):1584-1595. doi: 10.1002/mp.14720. Epub 2021 Feb 24.
Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data.
To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula mid-area) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented.
This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches.
The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.
在三维光学相干断层扫描（OCT）数据中准确分割眼睛的视网膜层可为临床诊断提供相关信息。本文描述了一种三维分割方法，该方法使用自适应的针对特定患者的视网膜图谱以及三维OCT数据的外观模型。
为了重建三维视网膜扫描图谱，首先对黄斑中心区域（黄斑中间区域）进行分割，在该区域可以清晰识别出中央凹。使用包括强度、空间信息以及12个视网膜层形状的马尔可夫吉布斯随机场（MGRF）对选定的视网膜中央凹区域进行分割。从200个不同个体收集的一组配准后的OCT扫描用于构建二维形状先验。随后将此形状先验适配到一阶外观和二阶空间交互MGRF模型。在分割黄斑中心“中央凹区域”后，利用已分割层的标签和外观对相邻切片进行分割。最后一步递归重复，直到对患者的三维OCT扫描进行分割。
该方法在50例正常和患有眼部疾病的患者中进行了测试。将分割结果与手动分割的真实情况进行比较。结果由临床视网膜专家进行验证。使用骰子相似系数（DSC）、95%双向修正豪斯多夫距离（HD）、无符号平均表面位置误差（MSPE）和平均体积差（AVD）指标来量化所提出方法的性能。结果证明所提出的方法比当前最先进的三维OCT方法更准确。
所提出的方法具有比当前最先进的三维OCT方法更快、更准确地分割所有12个视网膜层的优点。
