中文版 | English
Title

Multiple Consistency Supervision based Semi-supervised OCT Segmentation using Very Limited Annotations

Author
DOI
Publication Years
2022
ISSN
1050-4729
ISBN
978-1-7281-9682-4
Source Title
Pages
8483-8489
Conference Date
23-27 May 2022
Conference Place
Philadelphia, PA, USA
Abstract
Optical Coherence Tomography (OCT) is a rapidly growing and promising imaging technique, enabling non-invasive high-resolution visualization of biological tissues. Segmentation of tissue structures from OCT scans is essen-tial for disease diagnosis but remains challenging for the blurry boundaries and large volumes. Deep learning-based OCT segmentation algorithms always require large numbers of annotations for satisfying performance, which is hard to meet since manually labeling is time-consuming and labor-intensive. Therefore, we propose a novel semi-supervised OCT segmentation framework utilizing very few labeled scans, i.e., 5 samples, and abundant unlabeled data. Specifically, our framework con-sists of one shared encoder and two different decoder branches. For the two branches, we design a strong augmentation-consistent supervision module and a scaling transformation-consistent supervision module respectively to improve their generalization ability. Besides, cross consistency supervision with feature perturbations between two branches is proposed to incorporate their advantages for further regularization. With such multiple consistency supervision, we aim to enrich the diversity of unsupervised information so as to make full use of labeled and unlabeled data. Experimental results on a public retinal OCT dataset demonstrate the effectiveness of our method, achieving an average dice score of 87.25% in the case of only 5 labeled samples used. It outperforms the supervised baseline by 3.46% and the best semi-supervised model by 1.42% in our experiments.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Indexed By
EI Accession Number
20223312572016
EI Keywords
Computer vision ; Deep learning ; Diagnosis ; Image segmentation ; Supervised learning ; Tissue
ESI Classification Code
Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Computer Applications:723.5 ; Vision:741.2 ; Optical Devices and Systems:741.3
Scopus EID
2-s2.0-85136328522
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9812447
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/395621
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.The Chinese University of Hong Kong,Department of Electronic Engineering,Hong Kong SAR,Hong Kong
2.Shenzhen Key Laboratory of Robotics Perception and Intelligence,Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Shenzhen Research Institute of the Chinese University of Hong Kong,Shenzhen,518055,China
Recommended Citation
GB/T 7714
Lu,Ye,Shen,Yutian,Xing,Xiaohan,et al. Multiple Consistency Supervision based Semi-supervised OCT Segmentation using Very Limited Annotations[C],2022:8483-8489.
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