公开了乳腺癌细胞3D动态图像的完整分割标注，用于细胞追踪与形态分析。
Full segmentation annotations of 3D time-lapse microscopy images of MDA231 cells
- 三人手工标注两段3D时间序列，覆盖复杂形变的迁移细胞
- 标注一致性高于自动生成的银标准，与已知追踪标记高度吻合
- 适合训练/测试3D细胞分割模型，或研究动态生物结构
高质量、公开可用的图像与视频数据集分割标注对推动图像处理领域至关重要。特别是针对大量目标的体数据标注，耗时且具挑战性。本文首次公开发布基于MCF-7细胞（Fluo-C3DL-MDA231）的全3D时间序列分割标注，由三位独立标注者完成两个序列的标注。该工作补充了之前论文中因篇幅限制未包含的详细描述与实验：验证了标注结果与细胞追踪挑战赛（CTC）提供的追踪标记一致，且基于CTC二维金标准的分割精度处于人之间差异范围内。对比了自动生成的银标准，发现本研究标注更能准确反映原始图像复杂性。这些标注可用于细胞分割的训练与测试，或分析高度动态物体的3D形态。
原文摘要 · Abstract (English)
High-quality, publicly available segmentation annotations of image and video datasets are critical for advancing the field of image processing. In particular, annotations of volumetric images of a large number of targets are time-consuming and challenging. In (Melnikova, A., & Matula, P., 2025), we presented the first publicly available full 3D time-lapse segmentation annotations of migrating cells with complex dynamic shapes. Concretely, three distinct humans annotated two sequences of MDA231 human breast carcinoma cells (Fluo-C3DL-MDA231) from the Cell Tracking Challenge (CTC). This paper aims to provide a comprehensive description of the dataset and accompanying experiments that were not included in (Melnikova, A., & Matula, P., 2025) due to limitations in publication space. Namely, we show that the created annotations are consistent with the previously published tracking markers provided by the CTC organizers and the segmentation accuracy measured based on the 2D gold truth of CTC is within the inter-annotator variability margins. We compared the created 3D annotations with automatically created silver truth provided by CTC. We have found the proposed annotations better represent the complexity of the input images. The presented annotations can be used for testing and training cell segmentation, or analyzing 3D shapes of highly dynamic objects.
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