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The paper introduces ASE_Res_UNet, a U-Net variant incorporating a novel noise-adaptive attention mechanism based on Squeeze-and-Excitation (SE) modules, for robust microtubule segmentation in noisy microscopy images. They address annotation challenges by creating a synthetic dataset generation strategy and systematically evaluate loss functions to handle class imbalance. The proposed ASE_Res_UNet outperforms existing methods on both synthetic and real microscopy datasets, demonstrating superior segmentation performance and generalization to other curvilinear structures while maintaining a lightweight architecture.
A noise-adaptive attention mechanism lets a lightweight U-Net segment microtubules in noisy microscopy images better than heavier models, even generalizing to blood vessels and nerves.
Background Segmenting cytoskeletal filaments in microscopy images is essential for studying their roles in cellular processes such as cell division and intracellular transport. However, this task is highly challenging due to the fine, densely packed, and intertwined nature of these structures. Imaging limitations—noise, low contrast, and uneven fluorescence—further complicate analysis. While deep learning has advanced segmentation of large, well-defined biological structures, its performance often degrades under such adverse conditions. Additional challenges include obtaining precise annotations for curvilinear structures and managing severe class imbalance during training. Results We introduce a novel noise-adaptive attention mechanism that extends the Squeeze-and-Excitation (SE) module to dynamically adjust to varying noise levels. Integrated into a U-Net decoder with residual encoder blocks, this yields ASE_Res_UNet, a lightweight yet high-performance model. To address annotation challenges, we developed a synthetic dataset generation strategy that ensures accurate annotations of fine filaments in noisy images, producing a synthetic dataset with two difficulty levels for segmentation benchmarking. We systematically evaluated loss functions and metrics to mitigate class imbalance, ensuring robust performance assessment. ASE_Res_UNet effectively segmented microtubules in noisy synthetic images, outperforming its ablated variants. It also demonstrated superior segmentation compared to models with alternative attention mechanisms or distinct architectures, while requiring fewer parameters, making it efficient for resource-constrained environments. Evaluation on a newly curated real microscopy dataset and a recently reannotated dataset highlighted ASE_Res_UNet’s effectiveness in segmenting microtubules beyond synthetic images. For these datasets, ASE_Res_UNet was competitive with a recent synthetic data-driven approach that shares two cytoskeleton pretrained models. Importantly, ASE_Res_UNet generalised well to other curvilinear structures (blood vessels and nerves) across diverse imaging conditions. Conclusions This work advances microtubule segmentation through three key contributions: (1) Providing two benchmark datasets (synthetic and real), addressing a critical gap in standardised evaluation resources for this task; (2) Introducing ASE_Res_UNet, a lightweight yet robust model combining noise-adaptive attention with residual learning; (3) Validating competitive performance across synthetic and real microscopy data. Additionally, we demonstrated generalisation to diverse curvilinear structures, showcasing potential for broader applications in biological research and medical diagnosis.
