Parallel Double Snakes. Application to the segmentation of retinal layers in 2D-OCT for pathological subjects

Rossant, Florence; Bloch, Isabelle; Ghorbel, Itebeddine; Pâques, Michel

doi:10.1016/j.patcog.2015.06.009

Cited by 32 publications

(24 citation statements)

References 28 publications

(56 reference statements)

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“…Authors Year Preprocessing Segmentation Classification Nasrulloh et al [11] 2018 Yes Yes No Keller et al [26] 2016 Yes Yes No Miri et al [96] 2016 Yes Yes No Zhang et al [5] 2015 Yes Yes No Xu et al [27] 2013 Yes Yes No Liu et al [19] 2011 Yes Yes Yes Duan et al [43] 2017 Yes Yes No Sui et al [28] 2017 [106] 2017 Yes Yes No Athira et al [107] 2018 Yes Yes No Gopinath et al [108] 2017 No Yes No Dodo et al [109] 2019 Yes Yes No Duan et al [110] 2015 Yes Yes No Lang et al [111] 2017 Yes Yes No Niu et al [112] 2014 Yes Yes No Rossant et al [113] 2015 Yes Yes No Tian et al [114] 2015 Yes Yes No Huang et al [80] 2019 No Yes Yes Nath et al [82] 2018 Yes Yes Yes Hassan and Hassan [81] 2019 Yes Yes Yes Hassan et al [1] 2016 Yes Yes Yes Fang et al [115] 2017 Yes Yes Yes the B-scans [93,94]. The OCTID was the only publicly available database found with only cases of macular holes pathology [95].…”

Section: Acquisition Of Datamentioning

confidence: 99%

See 1 more Smart Citation

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Mendes¹,

Lucena²,

Lucena³

et al. 2020

AIS

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Macular holes are a blinding condition that occur due to overuse of the fovea, in which a hole alters the natural retinal structure. Optical Coherence Tomography (OCT) is a way of mapping and shaping retinal sections without physical contact and has become a powerful tool for diagnosing pathologies. This paper deals with a review of automated segmentation of macular holes in OCT images, detailing its varied possibilities. It may be considered something new, no reviews were made about the topic. The purpose of this review is to show the latest trends, through the approaches in preprocessing and segmentation. Recent studies were used to validate the research, 2011 onwards, from the Science Direct, IEEE, PubMed and Google scholar bases. The objectives, methodology, tools, database, advantages, disadvantages, validation metrics and results of the selected material are analyzed and mentioned. Based on this, techniques and their results are compared. From this, future outlook scenarios of automated segmentation of macular holes in OCT images are mentioned.

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Section: Acquisition Of Datamentioning

confidence: 99%

“…The overall DSC was of 91.25% and the mean and SD for unsigned boundaries were of 1.27 ± 1.06 pixels. Rossant [113] sugested a segmentation method of layers for RP subjects using another approach of snakes, the PDS. The database included 95 images.…”

Section: Selected Workmentioning

confidence: 99%

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Mendes¹,

Lucena²,

Lucena³

et al. 2020

AIS

View full text Add to dashboard Cite

show abstract

“…In this section, we will provide an overview of the state-of-the-art methods (i.e. parallel double snakes [14], Chiu's method [15], OCTRIMA3D [29,30], Dufour's method [27]) that will be compared with our proposed GDM in Section 3. For a complete review on the subject, we refer the reader to [39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Parallel double snakes (PDS): Rossant et al [14] detected the pathological (retinitis pigmentosa) cellular boundaries in B-scan images by minimising an energy functional that includes two parallel active parametric contours. Their proposed PDS model consists of a centreline C(s) = (x(s), y(s)) parametrised by s and two parallel curves C 1 (s) = C(s) + b(s)n(s) and C 2 (s) = C(s) − b(s)n(s) with b(s) being a spatially varying halfthickness and n(s) = (n x (s), n y (s)) the normal vector to the the centreline C(s).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance

Duan

Tench

Gottlob

et al. 2017

Pattern Recognition

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Optical coherence tomography (OCT) is a non-invasive imaging technique that can produce images of the eye at the microscopic level. OCT image segmentation to localise retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve disorders. In this paper, we introduce a novel and accurate geodesic distance method (GDM) for OCT segmentation of both healthy and pathological images in either two-or three-dimensional spaces. The method uses a weighted geodesic distance by an exponential function, taking into account both horizontal and vertical intensity variations. The weighted geodesic distance is efficiently calculated from an Eikonal equation via the fast sweeping method. The segmentation is then realised by solving an ordinary differential equation with the geodesic distance. The results of the GDM are compared with manually segmented retinal layer boundaries/surfaces. Extensive experiments demonstrate that the proposed GDM is robust to complex retinal structures with large curvatures and irregularities and it outperforms the parametric active contour algorithm as well as the graph theoretic based approaches for delineating the retinal layers in both healthy and pathological images.

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“…There exist rich literature on approaches for automatic and semi-automatic OCT image segmentation. Common methods include deformable models [1,2], graph-based and geodesic distance methods [3,4], statistical shape and appearance models [5,6], etc. Very recently, deep neural networks are becoming increasingly popular for OCT segmentation, demonstrating excellent performance [7,8].…”

Section: Introductionmentioning

confidence: 99%

OCT Segmentation: Integrating Open Parametric Contour Model of the Retinal Layers and Shape Constraint to the Mumford-Shah Functional

Duan

Xie

Liu

et al. 2018

Shape in Medical Imaging

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In this paper, we propose a novel retinal layer boundary model for segmentation of optical coherence tomography (OCT) images. The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational functional is first defined to evolve the retinal layer boundary model to segment the 9 layers simultaneously. By making use of the normals of open parametric contours, we construct equal sized adjacent narrowbands that are divided by each contour. Regional information in each narrowband can thus be integrated into the MS energy functional such that its optimisation is robust against different initialisations. A statistical prior is also imposed on the shape of the segmented parametric contours for the functional. As such, by minimising the MS energy functional the parametric contours can be driven towards the true boundaries of retinal layers, while the similarity of the contours with respect to training OCT shapes is preserved. Experimental results on real OCT images demonstrate that the method is accurate and robust to low quality OCT images with low contrast and high-level speckle noise, and it outperforms the recent geodesic distance based method for segmenting 9 layers of the retina in OCT images.

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Parallel Double Snakes. Application to the segmentation of retinal layers in 2D-OCT for pathological subjects

Cited by 32 publications

References 28 publications

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance

OCT Segmentation: Integrating Open Parametric Contour Model of the Retinal Layers and Shape Constraint to the Mumford-Shah Functional

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