A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field. (October 2018)

• Record Type: Journal Article
• Authors: Chakravarty, Arunava; Sivaswamy, Jayanthi
• Is Part Of: Computer methods and programs in biomedicine. Volume 165(2018)
• Page Start: 235
• Page End: 250
• Publication Date: 2018-10
• Subjects: Optical coherence tomography -- Conditional random field -- Structured support vector machines -- Diabetic macular edema -- Age-Related macular degeneration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.09.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a method to jointly extract multiple retinal layer boundaries in OCT B-scans. The method is supervised and can be trained in an end-to-end manner. It has been evaluated in the presence of retinal pathologies. It works well on images acquired using different scanners and resolutions. The MATLAB implementation of our method has been publicly released for research use. Abstract: 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-RPEin 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.