Segmentation of choroid neovascularization in OCT images based on convolutional neural network with differential amplification blocks

Jinzhu Su; Xinjian Chen; Yuhui Ma; Weifang Zhu; Fei Shi

doi:10.1117/12.2548273

10 March 2020 Segmentation of choroid neovascularization in OCT images based on convolutional neural network with differential amplification blocks

Jinzhu Su, Xinjian Chen, Yuhui Ma, Weifang Zhu, Fei Shi

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131320 (2020) https://doi.org/10.1117/12.2548273
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Choroidal neovascularization(CNV) refers to abnormal choroidal vessels that grow through the Bruch’s membrane to the bottom of retinal pigment epithelium (RPE) or retinal neurepithelium (RNE) layer, which is the pathological characterization of age-related macular degeneration (AMD) and pathological myopia (PM). Nowadays, optical coherence tomography (OCT) is an important imaging modality for observing CNV. This paper creatively proposes a convolutional neural network with differential amplification blocks (DACNN) to segment CNV in OCT images. There are two main contributions. (1) A differential amplification block (DAB) is proposed to extract the contrast information of foreground and background. (2) The DAB is integrated into the U-shaped convolutional neural network for CNV segmentation. The method proposed in this paper was verified on a dataset composed of 886 OCT B-scans. Compared with manual segmentation, the mean Dice similarity coefficient can reach 86.40%, outperforming some existing deep networks for segmentation.

Citation Download Citation

Jinzhu Su, Xinjian Chen, Yuhui Ma, Weifang Zhu, and Fei Shi "Segmentation of choroid neovascularization in OCT images based on convolutional neural network with differential amplification blocks", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131320 (10 March 2020); https://doi.org/10.1117/12.2548273

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