Literature DB >> 28436839

Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging.

Menglin Wu, Qiang Chen, XiaoJun He, Ping Li, Wen Fan, SongTao Yuan, Hyunjin Park.   

Abstract

OBJECTIVE: Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging.
METHODS: In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans.
RESULTS: Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively.
CONCLUSION: We report a fully automatic method for the segmentation of subretinal fluid. SIGNIFICANCE: Our method shows the potential to improve clinical therapy for CSC.

Entities:  

Mesh:

Year:  2017        PMID: 28436839     DOI: 10.1109/TBME.2017.2695461

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  6 in total

1.  Intraretinal fluid identification via enhanced maps using optical coherence tomography images.

Authors:  Plácido L Vidal; Joaquim de Moura; Jorge Novo; Manuel G Penedo; Marcos Ortega
Journal:  Biomed Opt Express       Date:  2018-09-11       Impact factor: 3.732

2.  Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.

Authors:  Freerk G Venhuizen; Bram van Ginneken; Bart Liefers; Freekje van Asten; Vivian Schreur; Sascha Fauser; Carel Hoyng; Thomas Theelen; Clara I Sánchez
Journal:  Biomed Opt Express       Date:  2018-03-07       Impact factor: 3.732

3.  Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy.

Authors:  Menglin Wu; Wen Fan; Qiang Chen; Zhenlong Du; Xiaoli Li; Songtao Yuan; Hyunjin Park
Journal:  Biomed Opt Express       Date:  2017-08-29       Impact factor: 3.732

4.  Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.

Authors:  Zhenxi Song; Liangyu Xu; Jiang Wang; Reza Rasti; Ananth Sastry; Jianwei D Li; William Raynor; Joseph A Izatt; Cynthia A Toth; Lejla Vajzovic; Bin Deng; Sina Farsiu
Journal:  Am J Ophthalmol       Date:  2020-07-21       Impact factor: 5.258

5.  RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network.

Authors:  Loza Bekalo Sappa; Idowu Paul Okuwobi; Mingchao Li; Yuhan Zhang; Sha Xie; Songtao Yuan; Qiang Chen
Journal:  J Digit Imaging       Date:  2021-06-02       Impact factor: 4.903

6.  Livelayer: a semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images.

Authors:  Mansooreh Montazerin; Zahra Sajjadifar; Elias Khalili Pour; Hamid Riazi-Esfahani; Tahereh Mahmoudi; Hossein Rabbani; Hossein Movahedian; Alireza Dehghani; Mohammadreza Akhlaghi; Rahele Kafieh
Journal:  Sci Rep       Date:  2021-07-02       Impact factor: 4.379

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.