MTFlow: Time-Conditioned Flow Matching for Microtubule Segmentation in Noisy Microscopy Images

Microtubules are cytoskeletal filaments that play essential roles in many cellular processes and are key therapeutic targets in several diseases. Accurate segmentation of microtubule networks is critical for studying their organization and dynamics but remains challenging due to filament curvature, dense crossings, and image noise. We present MTFlow, a novel time-conditioned flow-matching model for microtubule segmentation. Unlike conventional U-Net variants that predict masks in a single pass, MTFlow learns vector fields that iteratively transport noisy masks toward the ground truth, enabling interpretable, trajectory-based refinement. Our architecture combines a U-Net backbone with temporal embeddings, allowing the model to capture the dynamics of uncertainty resolution along filament boundaries. We trained and evaluated MTFlow on synthetic and real microtubule datasets and assessed its generalization capability on public biomedical datasets of curvilinear structures such as retinal blood vessels and nerves. MTFlow achieves competitive segmentation accuracy comparable to state-of-the-art models, offering a powerful and time-efficient tool for filamentous structure analysis with more precise annotations than manual or semi-automatic approaches.

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.