Joint Optic Disc and Cup Segmentation Via KNN-Based Transformer and Deformable Aggregation Attention

Deep learning has advanced medical image segmentation, especially for optic disc (OD) and cup detection. While convolutional neural networks (CNNs) struggle with long-range dependencies, Transformer-based architectures have emerged to address this limitation.However, complete replacement of CNNs with Transformers may impair local feature extraction. Additionally, reliance on expert-annotated datasets makes supervised learning costly. To overcome these limitations, we present DAK-Former, a novel self-supervised contrastive learning approach for OD and cup segmentation. Our approach introduces a new attention mechanism that combines K-nearest neighbors (KNN) with Transformer components, along with a deformable aggregation attention module to improve global feature representation. Additionally, our method uses multiple types of medical imaging data, including MRI, CT scans, and X-rays. Using contrastive learning, we match encoded queries with a dictionary of encoded keys, allowing the network to learn meaningful unsupervised feature representations. We evaluated DAK-Former on two publicly available fundus image datasets and compared it with state-of-the-art methods. Experimental results show that DAK-Former is highly effective and consistently outperforms existing approaches.