Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild
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2026
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| author | Lu, Yanrui Chen, Danyang Xiao, Haowen Zhu, Jiarui Ge, Fukang Zou, Binqian Guan, Jiali Liang, Jiayin Wang, Yuting Guan, Ziqian Bao, Xiangcheng Bi, Jinhao Gu, Lin He, Jun Zhu, Yingying |
| author_facet | Lu, Yanrui Chen, Danyang Xiao, Haowen Zhu, Jiarui Ge, Fukang Zou, Binqian Guan, Jiali Liang, Jiayin Wang, Yuting Guan, Ziqian Bao, Xiangcheng Bi, Jinhao Gu, Lin He, Jun Zhu, Yingying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12464 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild Lu, Yanrui Chen, Danyang Xiao, Haowen Zhu, Jiarui Ge, Fukang Zou, Binqian Guan, Jiali Liang, Jiayin Wang, Yuting Guan, Ziqian Bao, Xiangcheng Bi, Jinhao Gu, Lin He, Jun Zhu, Yingying Computer Vision and Pattern Recognition 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. |
| title | Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12464 |