Automatic choroid layer segmentation in OCT images via context efficient adaptive network
|Corresponding Author||Zhang，Jiong; Zhao，Yitian|
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.
|WOS Accession No|
|EI Accession Number|
Adaptive optics ; Deep learning ; Diagnosis ; Image segmentation ; Medical imaging ; Ophthalmology ; Textures
|ESI Classification Code|
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
|ESI Research Field|
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
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.
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