Literature DB >> 26401595

Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography.

Jelena Novosel1, Gijs Thepass2, Hans G Lemij3, Johannes F de Boer4, Koenraad A Vermeer5, Lucas J van Vliet6.   

Abstract

Optical coherence tomography (OCT) yields high-resolution, three-dimensional images of the retina. Reliable segmentation of the retinal layers is necessary for the extraction of clinically useful information. We present a novel segmentation method that operates on attenuation coefficients and incorporates anatomical knowledge about the retina. The attenuation coefficients are derived from in-vivo human retinal OCT data and represent an optical property of the tissue. Then, the layers in the retina are simultaneously segmented via a new flexible coupling approach that exploits the predefined order of the layers. The accuracy of the method was evaluated on 20 peripapillary scans of healthy subjects. Ten of those subjects were imaged again to evaluate the reproducibility. An additional evaluation was performed to examine the robustness of the method on a variety of data: scans of glaucoma patients, macular scans and scans by a two different OCT imaging devices. A very good agreement on all data was found between the manual segmentation performed by a medical doctor and the segmentation obtained by the automatic method. The mean absolute deviation for all interfaces in all data types varied between 1.9 and 8.5 µm (0.5-2.2 pixels). The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Attenuation coefficients; Bayes; Glaucoma; Macula; Retinal nerve fibre layer

Mesh:

Year:  2015        PMID: 26401595     DOI: 10.1016/j.media.2015.08.008

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  9 in total

1.  Active contour method for ILM segmentation in ONH volume scans in retinal OCT.

Authors:  Kay Gawlik; Frank Hausser; Friedemann Paul; Alexander U Brandt; Ella Maria Kadas
Journal:  Biomed Opt Express       Date:  2018-11-28       Impact factor: 3.732

2.  Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images.

Authors:  Brenton Keller; David Cunefare; Dilraj S Grewal; Tamer H Mahmoud; Joseph A Izatt; Sina Farsiu
Journal:  J Biomed Opt       Date:  2016-07-01       Impact factor: 3.170

3.  Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

Authors:  Yufan He; Aaron Carass; Yihao Liu; Bruno M Jedynak; Sharon D Solomon; Shiv Saidha; Peter A Calabresi; Jerry L Prince
Journal:  Biomed Opt Express       Date:  2019-09-12       Impact factor: 3.732

4.  Structured layer surface segmentation for retina OCT using fully convolutional regression networks.

Authors:  Yufan He; Aaron Carass; Yihao Liu; Bruno M Jedynak; Sharon D Solomon; Shiv Saidha; Peter A Calabresi; Jerry L Prince
Journal:  Med Image Anal       Date:  2020-10-14       Impact factor: 8.545

5.  Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.

Authors:  Bhavna Josephine Antony; Byung-Jin Kim; Andrew Lang; Aaron Carass; Jerry L Prince; Donald J Zack
Journal:  PLoS One       Date:  2017-08-17       Impact factor: 3.240

6.  DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Authors:  Qiaoliang Li; Shiyu Li; Zhuoying He; Huimin Guan; Runmin Chen; Ying Xu; Tao Wang; Suwen Qi; Jun Mei; Wei Wang
Journal:  Transl Vis Sci Technol       Date:  2020-12-09       Impact factor: 3.283

7.  Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator.

Authors:  Jian Liu; Shixin Yan; Nan Lu; Dongni Yang; Hongyu Lv; Shuanglian Wang; Xin Zhu; Yuqian Zhao; Yi Wang; Zhenhe Ma; Yao Yu
Journal:  Sci Rep       Date:  2022-01-26       Impact factor: 4.996

8.  Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging.

Authors:  Rafael Berenguer-Vidal; Rafael Verdú-Monedero; Juan Morales-Sánchez; Inmaculada Sellés-Navarro; Rocío Del Amor; Gabriel García; Valery Naranjo
Journal:  Sensors (Basel)       Date:  2021-12-01       Impact factor: 3.576

9.  A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP).

Authors:  Yi-Zhong Wang; Wenxuan Wu; David G Birch
Journal:  Transl Vis Sci Technol       Date:  2021-11-01       Impact factor: 3.283

  9 in total

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