Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations

Abstract

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.

Kinetic Analysis of the Motility of Giant Virus-Infected Amoebae Using Phase-Contrast Microscopic Images

“…Automated cell tracking is a useful tool for the discovery of hidden biological insights in large datasets and the elucidation of physiological and microbiological processes (Gilbert et al, 2010;Masuzzo et al, 2016;Ker et al, 2018). In multicellular organisms, the migration of cells plays a fundamental role in various cellular processes, including the immune response, formation of the neural network, and cancer metastasis.…”

“…In multicellular organisms, the migration of cells plays a fundamental role in various cellular processes, including the immune response, formation of the neural network, and cancer metastasis. Thus, algorithms have been reported that track muscle stem cells automatically or manually and reveal cell lineage relationships (Gilbert et al, 2010;Ker et al, 2018). Additionally, in unicellular organisms, cell motilities reveal their behaviors in their own populations and ecosystems (Mathijssen et al, 2019).…”

Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency

“…5, the heatmaps of our method become more clear than the baseline even in frames away from the labeled frame. Evaluation on C2C12: To further evaluate our method on a more challenging case, we used a public dataset [10] (C2C12), which consists of 48 sequences with 1,013 images respectively. C2C12 cell is captured by phase-contrast microscopy under four different media conditions (Control, FGF2, BMP2, FGF2+BMP2) at a resolution of 1040 × 1392 pixels.…”

Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response

“…We evaluated our methods by using three challenging data-set with annotation that cells were cultured in dense and captured by different microscopy as shown in Fig. 3 (a); 1) C2C12: myoblast cells captured by phase contrast microscopy [3] C2C12 NSC…”