ContourFD-Net: A Finite-Difference-Driven Contour Attention Network for Efficient Medical Image Segmentation on Edge Devices

Abstract

Accurate medical image segmentation remains challenging due to complex anatomical structures and ambiguous boundaries. To address the loss of structural cues in existing methods, we propose ContourFD-Net, a gradient-guided attention network. Unlike existing contour-aware networks that rely on auxiliary supervision, ContourFD-Net introduces an explicit structural prior via fixed finite-difference operators and couples gradient modeling with attention-based feature refinement. This approach preserves fine details through a dual-path architecture and decoupled spatial–channel attention. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of the proposed method. On the DSB 2018 dataset, ContourFD-Net achieves a Dice coefficient of 91.67%, demonstrating superior performance compared with representative state-of-the-art methods. Moreover, the framework requires only 6.40 M parameters and 3.39 GFLOPs, demonstrating a favorable accuracy–efficiency tradeoff. On the NVIDIA Jetson Orin NX platform, ContourFD-Net achieves an average inference latency of 16.97 ms and a throughput of 59.05 queries per second (QPS) with only 49-MiB memory consumption, validating its efficiency for real-time deployment on resource-constrained edge devices. The source code and model weights are available at https://github.com/ZBKim/ContourFD-Net