🤖 AI Summary
High-quality, publicly available segmentation annotations for dynamic 3D morphological modeling of migrating MDA-MB-231 cells remain scarce, hindering benchmarking and development of accurate 3D live-cell segmentation methods.
Method: We introduce the first large-scale, manually curated 3D time-lapse microscopy segmentation dataset with comprehensive spatiotemporal annotations. Annotations integrate Cell Tracking Challenge lineage labels and 2D ground-truth masks to ensure cross-dimensional consistency, validated against automated “silver-standard” segmentations. Inter-annotator variability analysis confirms high reproducibility and fidelity in capturing complex cellular deformations and motility.
Contribution/Results: This dataset fills a critical gap in 3D live-cell dynamic segmentation benchmarks. It enables robust training and evaluation of deep learning segmentation models and facilitates quantitative 3D morphodynamic analysis, establishing a new standard for rigorous, data-driven investigation of cellular dynamics.
📝 Abstract
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.