Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10468
Title: LiDeOCTNet: A lightweight OCT-aware framework for segmentation of irregularly layered tissue structures
Authors: Abaxi, Sai Mu Dalike 
Dr. NAWAZ Mehmood 
Shi, Peilun 
Wei, Hao 
Abbasi, Syeda Aimen 
Yuan, Wu 
Issue Date: 2023
Source: IEEE Transactions on Biomedical Engineering, 2023, vol. 70(3).
Journal: IEEE Transactions on Biomedical Engineering 
Abstract: Abstract: An automated and lightweight method to accurately segment optical coherence tomography (OCT) images can bring a plethora of benefits, such as the production of objective diagnostic indicators at a fast rate and the implementation in imaging devices with ease. Due to the unique imaging principle, OCT images differ from natural images as they feature layered structures stretching along the image width, instead of completely closed regions. Conventional convolutional neural networks designed for natural images are usually sub-optimal for segmenting OCT images. Therefore, it is imperative to develop a segmentation network with a strong awareness of the structural features in OCT images for more efficient predictions. In this work, we introduce a novel lightweight deformable OCT segmentation network (LiDeOCTNet) to enable a flexible and scalable feature receptive field for an accurate segmentation of the irregular structures in OCT images. When compared with the classic UNet, LiDeOCTNet achieved better performance in segmenting both retinal and endoscopic OCT images. In comparison to the state-of-the-art networks, LiDeOCTNet offered competitive results with a far more lightweight network. The simplistic design of our network may lead to a feasible OCT-aware framework to achieve reliable segmentation of OCT images in real time.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10468
ISSN: 1558-2531
0018-9294
DOI: 10.36227/techrxiv.22308655.v1
Appears in Collections:Applied Data Science - Publication

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