2017
DOI: 10.1109/tmi.2017.2666045
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Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas

Abstract: Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the sig… Show more

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Cited by 52 publications
(33 citation statements)
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“…Several level-sets methods have also been employed in retinal layer segmentation, most of which are related to the macula region and, or specific pathologies in this region [30][31][32][33][34]. [32,33] use 2D segmentation, which limits their capabilities in capturing challenging 3D structures, as seen at the ONH.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Several level-sets methods have also been employed in retinal layer segmentation, most of which are related to the macula region and, or specific pathologies in this region [30][31][32][33][34]. [32,33] use 2D segmentation, which limits their capabilities in capturing challenging 3D structures, as seen at the ONH.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, most of these methods focus on the macula, except [31] which also provides data on ONH scans, although as the authors stated themselves, this algorithm would need further modifications to account for difficult topologies. To overcome the intensity variation and disruptive pathological changes caused by several diseases, [30] proposed a 3D approach steered by local differences in the signal between adjacent retina layers. It is not clear how this approach would perform at the ONH, especially in low contrast between vitreous and interface, since this might be the reason why drusen, especially small ones were not so accurately detected.…”
Section: Discussionmentioning
confidence: 99%
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“…The review indicates many types of preprocessing techniques: dilation and erosion [5], median filter [27,107,111], gaussian filter [11,100,101], wiener filter [11,81,99], binary image [26,100], gradient image [26,114], anisotropic difusion filter [5,97,99], image aligment [19,98,103], attenuation coefficient [101], enhanced contrast [1,105], image flattenig [106,114], resize the image [1,107], edge flow [112], sparse filter [112], normalization [81], green channel [81], greyscale [1], morphological operations [1] and others. In Figure 2 is shown the improvements of preprocessing applied to OCT image.…”
Section: Preprocessingmentioning
confidence: 99%
“…Related work In recent years, deep learning based and non deep learning based methods were applied on this task [3,4,5,6,7]. Generally it has been shown that deep learning based methods, namely convolutional neural networks (CNN), outperform the previous cost-function based models [3,6,7].…”
Section: Introductionmentioning
confidence: 99%