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.

翻译：微管是细胞骨架丝状结构，在许多细胞过程中发挥关键作用，也是多种疾病的重要治疗靶点。准确分割微管网络对于研究其组织结构和动态变化至关重要，但由于丝状结构曲率、密集交叉和图像噪声等因素，该任务仍面临挑战。本文提出MTFlow——一种用于微管分割的新型时间条件流匹配模型。与通过单次前向传播预测掩码的传统U-Net变体不同，MTFlow学习向量场，通过迭代方式将噪声掩码向真实标注传输，实现可解释的、基于轨迹的精细化分割。我们的架构将U-Net主干网络与时间嵌入相结合，使模型能够捕捉沿丝状边界不确定性消解的动态过程。我们在合成与真实微管数据集上训练并评估MTFlow，并在视网膜血管和神经等公开生物医学曲线结构数据集上验证其泛化能力。MTFlow实现了与最先进模型相当的竞争性分割精度，为丝状结构分析提供了强大高效的工具，其标注精度优于手动或半自动方法。