Full segmentation annotations of 3D time-lapse microscopy images of MDA231 cells
This provides a valuable dataset for researchers in biomedical image processing, enabling better testing and training of cell segmentation methods, though it is incremental as it builds on existing Cell Tracking Challenge data.
The authors tackled the lack of high-quality segmentation annotations for 3D time-lapse microscopy images by creating the first publicly available full 3D time-lapse segmentation annotations of migrating MDA231 cells, showing that the annotations are consistent with existing tracking markers and have segmentation accuracy within inter-annotator variability margins.
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.