A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field
A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field
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DOI：
10.1016/j.cmpb.2018.09.004
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发表时间：
2018-10-01
影响因子：
6.1
通讯作者：
Sivaswamy, Jayanthi
中科院分区：
文献类型：
--
作者：
Chakravarty, Arunava;Sivaswamy, Jayanthi
背景与目的：光学相干断层扫描（OCT）图像中视网膜内组织层的准确分割对老年性黄斑变性（AMD）、糖尿病性黄斑水肿（DME）等眼部疾病的诊断和治疗具有重要意义。现有的基于能量最小化的方法采用了多个手工制作的成本条款，并且经常在存在病变的情况下失败。在这项工作中，我们以端到端方式从训练图像中学习能量，从而消除了手工处理能量的需要。通过在适当的数据集上重新训练，我们的方法可以很容易地适应病理。方法：我们提出了一个条件随机场（CRF）框架，用于OCT b扫描的联合多层分割。每个视网膜层和边界的外观由两个卷积滤波器组建模，形状先验使用高斯分布建模。总CRF能量是线性参数化的，通过使用结构化支持向量机公式来进行联合的端到端训练。结果：该方法在4个公共数据集上优于3种基准算法。NORMAL-1和NORMAL-2数据集包含健康的OCT b扫描，而AMD-1和DME-1数据集分别包含AMD和DME病例的b扫描。该方法在NORMAL-1、NORMAL-2、NORMAL-1和DME-1组合数据集上的平均无符号边界定位误差（U-BLE）分别为1.52像素、1.11像素和2.04像素，均优于三种基准方法。在NORMAL-1上的Dice系数为0.87，在NORMAL-2上的Dice系数为0.89，在NORMAL-1和DME-1联合数据集上的Dice系数为0.84。在NORMAL-1和AMD-1组合数据集上，我们在ILM，内部和外部RPE边界上实现了1.86像素的平均U-BLE， ILM-RPE区域的Dice为0.98，RPE层的Dice为0.81。结论：我们提出了一种基于监督CRF的OCT图像多组织层联合分割方法。它可以帮助眼科医生定量分析视网膜组织层的结构变化，为临床实践和大规模临床研究提供依据。(c) 2018 Elsevier B.V.版权所有
Background and Objective: Accurate segmentation of the intra-retinal tissue layers in Optical Coherence Tomography (OCT) images plays an important role in the diagnosis and treatment of ocular diseases such as Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). The existing energy minimization based methods employ multiple, manually handcrafted cost terms and often fail in the presence of pathologies. In this work, we eliminate the need to handcraft the energy by learning it from training images in an end-to-end manner. Our method can be easily adapted to pathologies by re-training it on an appropriate dataset.Methods: We propose a Conditional Random Field (CRF) framework for the joint multi-layer segmentation of OCT B-scans. The appearance of each retinal layer and boundary is modeled by two convolutional filter banks and the shape priors are modeled using Gaussian distributions. The total CRF energy is linearly parameterized to allow a joint, end-to-end training by employing the Structured Support Vector Machine formulation.Results: The proposed method outperformed three benchmark algorithms on four public datasets. The NORMAL-1 and NORMAL-2 datasets contain healthy OCT B-scans while the AMD-1 and DME-1 dataset contain B-scans of AMD and DME cases respectively. The proposed method achieved an average unsigned boundary localization error (U-BLE) of 1.52 pixels on NORMAL-1, 1.11 pixels on NORMAL-2 and 2.04 pixels on the combined NORMAL-1 and DME-1 dataset across the eight layer boundaries, outperforming the three benchmark methods in each case. The Dice coefficient was 0.87 on NORMAL-1, 0.89 on NORMAL-2 and 0.84 on the combined NORMAL-1 and DME-1 dataset across the seven retinal layers. On the combined NORMAL-1 and AMD-1 dataset, we achieved an average U-BLE of 1.86 pixels on the ILM, inner and outer RPE boundaries and a Dice of 0.98 for the ILM-RPE in region and 0.81 for the RPE layer.Conclusion: We have proposed a supervised CRF based method to jointly segment multiple tissue layers in OCT images. It can aid the ophthalmologists in the quantitative analysis of structural changes in the retinal tissue layers for clinical practice and large-scale clinical studies. (c) 2018 Elsevier B.V. All rights reserved.