Abstract
Accurate delineation of individual cells in microscopy videos is essential for studying cellular dynamics, yet separating touching or overlapping instances remains a persistent challenge. Although foundation-model for segmentation such as SAM have broadened the accessibility of image segmentation, they still struggle to separate nearby cell instances in dense microscopy scenes without extensive prompting. We propose a prompt-free, boundary-aware instance segmentation framework that predicts signed distance functions (SDFs) instead of binary masks, enabling smooth and geometry-consistent modeling of cell contours. A learned sigmoid mapping converts SDFs into probability maps, yielding sharp boundary localization and robust separation of adjacent instances. Training is guided by a unified Modified Hausdorff Distance (MHD) loss that integrates region- and boundary-based terms. Evaluations on both public and private high-throughput microscopy datasets demonstrate improved boundary accuracy and instance-level performance compared to recent SAM-based and foundation-model approaches. Source code is available at: this https URL
Abstract (translated)
在显微视频中精确勾画单个细胞对于研究细胞动态至关重要，但分离接触或重叠的细胞实例仍是一项长期挑战。尽管SAM等基础分割模型已拓宽了图像分割的可及性，但在密集显微场景中，它们仍难以在无需大量提示的情况下分离邻近的细胞实例。我们提出了一种无需提示、边界感知的实例分割框架，该框架预测符号距离函数而非二值掩码，从而实现对细胞轮廓平滑且几何一致的建模。通过学习的sigmoid映射将符号距离函数转换为概率图，实现了相邻实例间清晰的边界定位与稳健分离。训练过程由统一的改进豪斯多夫距离损失指导，该损失整合了区域与边界相关项。在公共及私有高通量显微数据集上的评估表明，与近期基于SAM及基础模型的方法相比，本方法在边界精度与实例级性能上均有提升。源代码见：此https链接
URL
https://arxiv.org/abs/2603.21206
