构建超大规模细胞器实例分割数据集，破解真实电镜图像中的复杂形态挑战。
Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild
- 设计连通性感知的标签传播算法生成10万+张真实电镜图像标注
- 发现现有模型在分布式细胞器（如内质网）上表现差，泛化能力弱
- 适合生物医学图像分析、细胞结构建模及长程依赖建模研究者使用
电子显微镜中细胞器的精确实例级分割对亚细胞形态和细胞器间相互作用的定量分析至关重要。然而，现有基于小规模、精修数据集的基准无法捕捉真实电镜数据中固有的异质性和大空间上下文，导致当前基于图像块的方法存在根本局限。为此，我们构建了一个大规模、多源的多细胞器实例分割基准，包含超过10万张2D EM图像，覆盖多种细胞类型与五类细胞器，真实反映现实多样性。标注由我们设计的3D连通性感知标签传播算法（3D LPA）生成，并经专家校验。我们进一步评测了U-Net、SAM变体和Mask2Former等先进模型。结果表明：现有模型难以在异构电镜数据中泛化，尤其在具有全局分布形态的细胞器（如内质网）上表现不佳。这些发现凸显了局部上下文模型与长程结构连续性建模之间的根本不匹配。该基准数据集与标注工具将不久后公开。
原文摘要 · Abstract (English)
Accurate instance-level segmentation of organelles in electron microscopy (EM) is critical for quantitative analysis of subcellular morphology and inter-organelle interactions. However, current benchmarks, based on small, curated datasets, fail to capture the inherent heterogeneity and large spatial context of in-the-wild EM data, imposing fundamental limitations on current patch-based methods. To address these limitations, we developed a large-scale, multi-source benchmark for multi-organelle instance segmentation, comprising over 100,000 2D EM images across variety cell types and five organelle classes that capture real-world variability. Dataset annotations were generated by our designed connectivity-aware Label Propagation Algorithm (3D LPA) with expert refinement. We further benchmarked several state-of-the-art models, including U-Net, SAM variants, and Mask2Former. Our results show several limitations: current models struggle to generalize across heterogeneous EM data and perform poorly on organelles with global, distributed morphologies (e.g., Endoplasmic Reticulum). These findings underscore the fundamental mismatch between local-context models and the challenge of modeling long-range structural continuity in the presence of real-world variability. The benchmark dataset and labeling tool will be publicly released soon.
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