Automated segmentation of intraretinal layers from macular optical coherence tomography images

Haeker, Mona; Sonka, Milan; Kardon, Randy H.; Shah, Vinay; Wu, Xiaodong; Abràmoff, Michael D.

doi:10.1117/12.710231

Cited by 37 publications

(42 citation statements)

References 14 publications

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“…, is assigned such that the total weight of a closed set in graph G equals to the region energy term 2 i=0 E region (R i ) (with a constant difference) [15]:…”

Section: Incorporation Of Region Informationmentioning

confidence: 99%

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Song

Liu

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm.

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“…, is assigned such that the total weight of a closed set in graph G equals to the region energy term 2 i=0 E region (R i ) (with a constant difference) [15]:…”

Section: Incorporation Of Region Informationmentioning

confidence: 99%

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Song

Liu

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

Self Cite

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“…These filtering methods were controlled by subjectively selected parameters and had difficulties in 'balancing' the deduction of high speckle noise and preservation of structural edges, especially in images with low contrast. Haeker et al [19] and Garvin et al [22], on the other hand, proposed image averaging to create composite images from repeat scans. The composite images had higher signal-to-noise ratio, but multiple scans (six repeat scans in this case) were needed, which may exacerbate the detrimental effects of eye movement between the scans.…”

Section: Introductionmentioning

confidence: 99%

“…Koozekanani et al [18] utilized a Markov model to select and organize the edges to form a coherent boundary structure. A minimum-cost closed set approach was developed by Haeker et al [19] and Niemeijer et al [20] to identify retinal layers based on a linear combination of domain-specific cost functions. Mishra et al [21] used the image gradient to derive an external force through an adaptive kernel function and used dynamic programming to identify the continuous retinal layers within the OCT images.…”

Section: Introductionmentioning

confidence: 99%

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

et al. 2010

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“…To increase the signal to noise ratio on the macular OCT images, up to six raw macular series are first aligned and registered together using the methods described in [2]. This results in a composite 3-D scan for each eye.…”

Section: Segmentation Overviewmentioning

confidence: 99%

“…To address this need, we have previously reported an optimal 3-D graph search approach for the intraretinal layer segmentation of macular scans [2,3]. The approach is based on the optimal graph search method reported by Wu and Chen [4] and the extension to the multiple surface case by Li et al [5].…”

Section: Introductionmentioning

confidence: 99%

Use of Varying Constraints in Optimal 3-D Graph Search for Segmentation of Macular Optical Coherence Tomography Images

Haeker

Abràmoff

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. An optimal 3-D graph search approach designed for simultaneous multiple surface detection is extended to allow for varying smoothness and surface interaction constraints instead of the traditionally used constant constraints. We apply the method to the intraretinal layer segmentation of 24 3-D optical coherence tomography (OCT) images, learning the constraints from examples in a leave-one-subject-out fashion. Introducing the varying constraints decreased the mean unsigned border positioning errors (mean error of 7.3 ± 3.7 μm using varying constraints compared to 8.3 ± 4.9 μm using constant constraints and 8.2 ± 3.5 μm for the inter-observer variability).

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Automated segmentation of intraretinal layers from macular optical coherence tomography images

Cited by 37 publications

References 14 publications

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Use of Varying Constraints in Optimal 3-D Graph Search for Segmentation of Macular Optical Coherence Tomography Images

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