Automatic choroid layer segmentation in OCT images via context efficient adaptive network

Yan，Qifeng1; Gu，Yuanyuan1; Zhao，Jinyu1; Wu，Wenjun1; Ma，Yuhui1; Liu，Jiang2; Zhang，Jiong1; Zhao，Yitian1

2022

10.1007/s10489-022-03723-w

APPLIED INTELLIGENCE   影响因子和分区

0924-669X

1573-7497

Optical Coherence Tomography (OCT) is a non-invasive and newly-developing technique to image human retina and choroid. Many ocular diseases such as pathological myopia and Age-related Macular Degeneration (AMD) are related to the morphological changes of the choroid. Consequently, the automatic choroid segmentation becomes an important step to the examination and diagnosis of those choroid-related diseases. However, there are still challenges such as the inseparability of the histogram between the choroid and sclera boundaries and the inconsistency of the choroid layer texture and intensity. To solve those challenges, we propose a Context Efficient Adaptive network (CEA-Net) that includes a module of Efficient Channel Attention (ECA), a novel block called adaptive morphological refinement (AMR) and a new loss function called Choroidal Convex Boundary (CCB) regularization. The Adaptive Morphological Refinement (AMR) block is designed to avoid the segmentation of discrete subtle objects in choroid. The new Choroidal Convex Boundary (CCB) loss is proposed to refine the segmented choroidal boundaries. The proposed method is applied to two OCT datasets acquired from two different manufacturers respectively in order to evaluate its effectiveness. The results show that the AMR block and CCB loss function enable the deep network to obtain more accurate choroid segmentations. In addition, for the first time in the field of medical image analysis, we construct a dedicated OCT choroid layer segmentation dataset (OCHID), which consists of 640 OCT images with choroidal boundaries annotations. This dataset is available for public use to assist community researchers in their research on related topics.

Choroidal layer segmentation Deep learning OCT

SCI ; EI

英语

其他

National Science Foundation of China[62103398] ; General research program of Zhejiang Provincial Department of health[2021PY073] ; Traditional Chinese Medicine project of Zhejiang Province[2021ZB268] ; Ningbo Natural Science Foundation[2021J028,202003N4039,202003N4040] ; Zhejiang Provincial Natural Science Foundation of China["LR22F020008","LZ19F010001"] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[2021298]

Computer Science

Computer Science, Artificial Intelligence

WOS:000818093600001

SPRINGER

20222612299383

Adaptive optics ; Deep learning ; Diagnosis ; Image segmentation ; Medical imaging ; Ophthalmology ; Textures

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Light/Optics:741.1 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746

ENGINEERING

2-s2.0-85132979169

Scopus

1.Cixi Institute of Biomedical Engineering,Ningbo Institute of Industrial Technology,Chinese Academy of Sciences,Zhejiang,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Yan，Qifeng,Gu，Yuanyuan,Zhao，Jinyu,et al. Automatic choroid layer segmentation in OCT images via context efficient adaptive network[J]. APPLIED INTELLIGENCE,2022,53(5):5554-5566.

Yan，Qifeng.,Gu，Yuanyuan.,Zhao，Jinyu.,Wu，Wenjun.,Ma，Yuhui.,...&Zhao，Yitian.(2022).Automatic choroid layer segmentation in OCT images via context efficient adaptive network.APPLIED INTELLIGENCE,53(5),5554-5566.

Yan，Qifeng,et al."Automatic choroid layer segmentation in OCT images via context efficient adaptive network".APPLIED INTELLIGENCE 53.5(2022):5554-5566.