Cyst Identification in Retinal Optical Coherence Tomography Images Using Hidden Markov Model

Overview

Journal Sci Rep

Specialty Science

Date 2023 Jan 2

PMID 36593300

Authors

Niloofarsadat Mousavi

Maryam Monemian

Parisa Ghaderi Daneshmand

Mohammad Mirmohammadsadeghi

Maryam Zekri

Hossein Rabbani

Affiliations

Soon will be listed here.

Abstract

Optical Coherence Tomography (OCT) is a useful imaging modality facilitating the capturing process from retinal layers. In the salient diseases of retina, cysts are formed in retinal layers. Therefore, the identification of cysts in the retinal layers is of great importance. In this paper, a new method is proposed for the rapid detection of cystic OCT B-scans. In the proposed method, a Hidden Markov Model (HMM) is used for mathematically modelling the existence of cyst. In fact, the existence of cyst in the image can be considered as a hidden state. Since the existence of cyst in an OCT B-scan depends on the existence of cyst in the previous B-scans, HMM is an appropriate tool for modelling this process. In the first phase, a number of features are extracted which are Harris, KAZE, HOG, SURF, FAST, Min-Eigen and feature extracted by deep AlexNet. It is shown that the feature with the best discriminating power is the feature extracted by AlexNet. The features extracted in the first phase are used as observation vectors to estimate the HMM parameters. The evaluation results show the improved performance of HMM in terms of accuracy.

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References

Venhuizen F, van Ginneken B, Liefers B, van Asten F, Schreur V, Fauser S . Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. Biomed Opt Express. 2018; 9(4):1545-1569. PMC: 5905905. DOI: 10.1364/BOE.9.001545. View

Chen X, Niemeijer M, Zhang L, Lee K, Abramoff M, Sonka M . Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut. IEEE Trans Med Imaging. 2012; 31(8):1521-31. PMC: 3659794. DOI: 10.1109/TMI.2012.2191302. View

Chiu S, Allingham M, Mettu P, Cousins S, Izatt J, Farsiu S . Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed Opt Express. 2015; 6(4):1172-94. PMC: 4399658. DOI: 10.1364/BOE.6.001172. View

Fang L, Wang C, Li S, Rabbani H, Chen X, Liu Z . Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification. IEEE Trans Med Imaging. 2019; 38(8):1959-1970. DOI: 10.1109/TMI.2019.2898414. View

Schlegl T, Waldstein S, Bogunovic H, Endstrasser F, Sadeghipour A, Philip A . Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology. 2017; 125(4):549-558. DOI: 10.1016/j.ophtha.2017.10.031. View

Montuoro A, Waldstein S, Gerendas B, Schmidt-Erfurth U, Bogunovic H . Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express. 2017; 8(3):1874-1888. PMC: 5480585. DOI: 10.1364/BOE.8.001874. View

Zheng Y, Sahni J, Campa C, Stangos A, Raj A, Harding S . Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina. Am J Ophthalmol. 2012; 155(2):277-286.e1. DOI: 10.1016/j.ajo.2012.07.030. View

Oguz I, Abramoff M, Zhang L, Lee K, Zhang E, Sonka M . 4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans. Invest Ophthalmol Vis Sci. 2016; 57(9):OCT621-OCT630. PMC: 5215413. DOI: 10.1167/iovs.15-18924. View

Rashno A, Koozekanani D, Drayna P, Nazari B, Sadri S, Rabbani H . Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms. IEEE Trans Biomed Eng. 2017; 65(5):989-1001. DOI: 10.1109/TBME.2017.2734058. View

10.

Fujimoto J, Pitris C, Boppart S, Brezinski M . Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia. 2000; 2(1-2):9-25. PMC: 1531864. DOI: 10.1038/sj.neo.7900071. View

11.

Roy A, Conjeti S, Karri S, Sheet D, Katouzian A, Wachinger C . ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express. 2017; 8(8):3627-3642. PMC: 5560830. DOI: 10.1364/BOE.8.003627. View

12.

Hassan T, Akram M, Masood M, Yasin U . Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans. Comput Biol Med. 2019; 105:112-124. DOI: 10.1016/j.compbiomed.2018.12.015. View

13.

Wang J, Zhang M, Pechauer A, Liu L, Hwang T, Wilson D . Automated volumetric segmentation of retinal fluid on optical coherence tomography. Biomed Opt Express. 2016; 7(4):1577-89. PMC: 4929662. DOI: 10.1364/BOE.7.001577. View

14.

Girish G, Thakur B, Chowdhury S, Kothari A, Rajan J . Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model. IEEE J Biomed Health Inform. 2018; 23(1):296-304. DOI: 10.1109/JBHI.2018.2810379. View

15.

Fujimoto J, Drexler W, Schuman J, Hitzenberger C . Optical Coherence Tomography (OCT) in ophthalmology: introduction. Opt Express. 2009; 17(5):3978-9. PMC: 5542846. DOI: 10.1364/oe.17.003978. View

16.

Cabrera Fernandez D . Delineating fluid-filled region boundaries in optical coherence tomography images of the retina. IEEE Trans Med Imaging. 2005; 24(8):929-45. DOI: 10.1109/TMI.2005.848655. View

17.

Quellec G, Lee K, Dolejsi M, Garvin M, Abramoff M, Sonka M . Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula. IEEE Trans Med Imaging. 2010; 29(6):1321-30. PMC: 2911793. DOI: 10.1109/TMI.2010.2047023. View

18.

Girish G, Saikumar B, Roychowdhury S, Kothari A, Rajan J . Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation. Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2019:2027-2031. DOI: 10.1109/EMBC.2019.8857333. View

19.

de Moura J, Vidal P, Novo J, Rouco J, Penedo M, Ortega M . Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images. Sensors (Basel). 2020; 20(7). PMC: 7180444. DOI: 10.3390/s20072004. View

20.

Wilkins G, Houghton O, Oldenburg A . Automated segmentation of intraretinal cystoid fluid in optical coherence tomography. IEEE Trans Biomed Eng. 2012; 59(4):1109-14. PMC: 3725742. DOI: 10.1109/TBME.2012.2184759. View