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

Autor: He Y; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: yhe35@jhu.edu., Carass A; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA., Liu Y; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA., Jedynak BM; Department of Mathematics & Statistics, Portland State University, Portland, OR 97201, USA., Solomon SD; Wilmer Eye Institute, The Johns Hopkins University School of Medicine, MD 21287, USA., Saidha S; Department of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA., Calabresi PA; Department of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA., Prince JL; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2021 Feb; Vol. 68, pp. 101856. Date of Electronic Publication: 2020 Oct 14.
DOI: 10.1016/j.media.2020.101856
Abstrakt: Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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