Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
This addresses the challenge of expensive pixel-wise annotation in medical imaging, offering a method to enhance segmentation with limited labeled data, though it appears incremental as it builds on existing semi-supervised techniques.
The paper tackles the problem of limited annotated data for medical image segmentation by proposing a semi-supervised approach using a novel pseudo-label called self-loop uncertainty, which approximates uncertainty estimation from multiple models with reduced inference time, achieving improved segmentation accuracy on two public datasets.
Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks for medical image segmentation. However, since the pixel-wise annotation of medical images is laborious and expensive, the amount of annotated data is usually deficient to well-train a neural network. In this paper, we propose a semi-supervised approach to train neural networks with limited labeled data and a large quantity of unlabeled images for medical image segmentation. A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy. The proposed self-loop uncertainty can be seen as an approximation of the uncertainty estimation yielded by ensembling multiple models with a significant reduction of inference time. Experimental results on two publicly available datasets demonstrate the effectiveness of our semi-supervied approach.