无需提示即可精准分割重叠细胞，提升显微图像实例分割效果。
Boundary-Aware Instance Segmentation in Microscopy Imaging
- 用符号距离函数建模细胞边界，实现平滑几何一致的轮廓预测
- 在多个高通量显微数据集上边界精度和实例性能优于现有方法
- 适合需要自动分割密集细胞图像的研究者，尤其适用于无标注提示场景
在显微视频中准确划分单个细胞对于研究细胞动态至关重要，但分离触碰或重叠的细胞实例仍是长期挑战。尽管像SAM这样的基础模型拓宽了图像分割的可及性，但在密集显微场景中仍难以在无大量提示的情况下分离相邻细胞实例。我们提出一种免提示、边界感知的实例分割框架，通过预测符号距离函数（SDF）代替二值掩码，实现细胞轮廓的平滑且几何一致建模。通过学习的sigmoid映射将SDF转换为概率图，实现精确边界定位并稳健分离邻近实例。训练采用统一的改进汉德森距离（MHD）损失，融合区域与边界项。在公开和私有高通量显微数据集上的评估表明，相比近期基于SAM和基础模型的方法，该方法在边界精度和实例级性能上均有提升。源代码见：https://github.com/ThomasMendelson/BAISeg.git
原文摘要 · Abstract (English)
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: https://github.com/ThomasMendelson/BAISeg.git
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