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

Lu，Ye1; Shen，Yutian1; Xing，Xiaohan1; Meng，Max Q.H.1,2,3

10.1109/ICRA46639.2022.9812447

2022

1050-4729

978-1-7281-9682-4

Proceedings - IEEE International Conference on Robotics and Automation

8483-8489

23-27 May 2022

Philadelphia, PA, USA

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.

Training Image segmentation Annotations Perturbation methods Optical coherence tomography Imaging Retina

Others

English

EI

20223312572016

Computer vision ; Deep learning ; Diagnosis ; Image segmentation ; Supervised learning ; Tissue

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

2-s2.0-85136328522

Scopus

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

Lu，Ye,Shen，Yutian,Xing，Xiaohan,et al. Multiple Consistency Supervision based Semi-supervised OCT Segmentation using Very Limited Annotations[C],2022:8483-8489.