Uncertainty guided semi-supervised segmentation of retinal layers in OCT images
This addresses the challenge of costly and time-consuming annotation in biomedical imaging, particularly for retinal layer segmentation in OCT images, with incremental improvements in semi-supervised techniques.
The paper tackles the problem of limited labeled data for medical image segmentation by proposing an uncertainty-guided semi-supervised learning method using a student-teacher approach, which improves segmentation performance compared to fully supervised methods and matches expert annotator levels.
Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In this paper, we propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network using limited labeled samples and a large number of unlabeled images. First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning. The trained model is used to generate soft segmentation labels and uncertainty maps for the unlabeled set. The student model is then updated using the softly segmented samples and the corresponding pixel-wise confidence of the segmentation quality estimated from the uncertainty of the teacher model using a newly designed loss function. Experimental results on a retinal layer segmentation task show that the proposed method improves the segmentation performance in comparison to the fully supervised approach and is on par with the expert annotator. The proposed semi-supervised segmentation framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities where access to annotated medical images is challenging