In interactive image segmentation methods, users can participate in and influence the segmentation process through their interactions, such as scribbles or bounding boxes. Similarly, the process of deep interactive segmentation utilizes users’ interactions to guide the network to learn the target of interest. This article mainly considers mouse clicking, which is the simplest interaction mode. Then, how to effectively characterize the click interaction (we call this “click encoding”) and fuse the click-related information with the network are the key issues in a deep interactive segmentation framework. However, the current click encoding method concentrates only on the spatial information of the clicks, so the region affected by each click is difficult to control, and the stability of the network is therefore reduced. Therefore, we propose a feature-interactive map that builds a close relationship between interaction information and target semantics. The affected region of the feature interactive map is determined by semantic information. Furthermore, we introduce an interactive nonlocal block by embedding a feature-interactive map into a nonlocal block, so that the long-range dependencies of the interaction information can be captured. Finally, based on the early fusion strategy, the features of the interactive nonlocal block are fused with the high-level features, thus amplifying the impacts of position and semantics on the final prediction results. Comprehensive experiments demonstrate that our click embedding approach significantly boosts the efficiency of the network and achieves state-of-the-art performance.

Convolutional neural networks (CNNs) are the backbone of most modern interactive segmentation algorithms. However, the limited receptive field of CNNs restricts their ability to capture long-range semantic relationships. Recently, transformers have gained significant attention for their capacity to capture long-range dependencies. Nevertheless, CNNs still outperform Transformer in extracting local information. An effective interactive segmentation algorithm should accurately capture fine-grained local details alongside global semantic relationships. Therefore, we propose GLClick, a global–local click-based interactive image segmentation model that integrates local and global information through a novel fusion mechanism. We design an efficient global–local feature fusion module (GLFM) that integrates fine-grained features from various layers of ResNet50 with those from the Transformer feature pyramid. This approach maintains ResNet50’s ability to extract local features while effectively leveraging the Transformer to capture global context. Additionally, we enhance the multi-layer perceptron (MLP) to improve performance. Extensive experiments on diverse benchmark datasets demonstrate significant improvements in interactive image segmentation, confirming the effectiveness of our approach. Moreover, we conduct experiments on medical image datasets, further illustrating the model’s versatility and effectiveness across different domains.