Uncertainty-guided cross-level fusion network for retinal OCT image segmentation

Deep learning-based segmentation methods for optical coherence tomography (OCT) have demonstrated outstanding performance. However, the stochastic distribution of training data and the inherent limitations of deep neural networks introduce uncertainty into the segmentation process. Accurately estimating this uncertainty is essential for generating reliable confidence assessments and improving model predictions.

To address these challenges, we propose a novel uncertainty-guided cross-layer fusion network (UGCFNet) for retinal OCT segmentation. UGCFNet integrates uncertainty quantification into the training process of deep neural networks and leverages this uncertainty to enhance segmentation accuracy.

Our model employs an encoder-decoder architecture that quantitatively assesses uncertainty at multiple stages, directing the network's focus toward regions with higher uncertainty. By facilitating cross-layer feature fusion, UGCFNet enhances the comprehensive understanding of both semantic information and morphological details. Additionally, we incorporate an improved Bayesian neural network loss function alongside an uncertainty-aware loss function, enabling the network to effectively utilize these mechanisms for better uncertainty modeling.

We conducted extensive experiments on the publicly available AI-Challenger and OIMHS OCT segmentation datasets. The training, validation, and testing sets of the AI-Challenger dataset are comprised of 32, 8, and 43 OCT volumes, yielding a total of 4096, 1024, and 5504 B-scans, respectively. The training, validation, and testing sets of the OIMHS dataset consist of 100, 25, and 25 OCT volumes, resulting in 2,310, 798, and 751 B-scans, respectively. The results demonstrate that UGCFNet achieves state-of-the-art performance, with average Dice similarity coefficients of 79.47% and 93.22% on the respective datasets.

Our proposed UGCFNet significantly advances retinal OCT segmentation by integrating uncertainty guidance and cross-level feature fusion, offering more reliable and accurate segmentation outcomes.