Semi-supervised OCT lesion segmentation via transformation-consistent with uncertainty and self-deep supervision

Abstract

Optical coherence tomography (OCT) is a non-invasive, high-resolution ocular imaging technique with important implications for the diagnosis and management of retinal diseases. Automatic segmentation of lesions in OCT images is critical for assessing disease progression and treatment outcomes. However, existing methods for lesion segmentation require numerous pixel-wise annotations, which are difficult and time-consuming to obtain. To address this challenge, we propose a novel framework for semi-supervised OCT lesion segmentation, termed transformation-consistent with uncertainty and self-deep supervision (TCUS). To address the issue of lesion area blurring in OCT images and unreliable predictions from the teacher network for unlabeled images, an uncertainty-guided transformation-consistent strategy is proposed. Transformation-consistent is used to enhance the unsupervised regularization effect. The student network gradually learns from meaningful and reliable targets by utilizing the uncertainty information from the teacher network, to alleviate the performance degradation caused by potential errors in the teacher network’s prediction results. Additionally, self-deep supervision is used to acquire multi-scale information from labeled and unlabeled OCT images, enabling accurate segmentation of lesions of various sizes and shapes. Self-deep supervision significantly improves the accuracy of lesion segmentation in terms of the Dice coefficient. Experimental results on two OCT datasets demonstrate that the proposed TCUS outperforms state-of-the-art semi-supervised segmentation methods.