Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images

Segmenting curvilinear structures in fluorescence microscopy remains a challenging task, particularly under noisy conditions and in dense filament networks commonly seen in vivo. To address this, we created two original datasets consisting of hundreds of synthetic images of fluorescently labelled microtubules within cells. These datasets are precisely annotated and closely mimic real microscopy images, including realistic noise. The second dataset presents an additional challenge, by simulating varying fluorescence intensities along filaments that complicate segmentation. While deep learning has shown strong potential in biomedical image analysis, its performance often declines in noisy or low-contrast conditions. To overcome this limitation, we developed a novel advanced architecture: the Adaptive Squeeze-and-Excitation Residual U-Net (ASE_Res_UNet). This model enhanced the standard U-Net by integrating residual blocks in the encoder and adaptive SE attention mechanisms in the decoder. Through ablation studies and comprehensive visual and quantitative evaluations, ASE_Res_UNet consistently outperformed its variants, namely standard U-Net, ASE_UNet and Res_UNet architectures. These improvements, particularly in noise resilience and detecting fine, low-intensity structures, were largely attributed to the adaptive SE attention module that we created. We further benchmarked ASE_Res_UNet against various state-of-the-art models, and found it achieved superior performance on our most challenging dataset. Finally, the model also generalized well to real microscopy images of stained microtubules as well as to other curvilinear structures. Indeed, it successfully segmented retinal blood vessels and nerves in noisy or low-contrast biomedical images, demonstrating its strong potential for applications in disease diagnosis and treatment.