Glioma C6 dataset for cell segmentation

Training and test images of Glioma C6 cells imaged using phase-contrast microscopy for the task of cell segmentation.

The example shows a phase-contrast image of Glioma C6 cells and the manually annotated segmentation mask.

Data type: Phase-contrast images with corresponding annotations in COCO format.

Microscopy data type: 2D phase-contrast images recorded at 24 or 72-hour intervals after cell seeding.

Microscope: BestScope BS-2092 microscope in phase-contrast mode, equipped with 10× and 20× objective lenses.

Cell type: C6 glioma cells (rat glial tumor cells, ATCC CCL-107).

Image size: 2592 × 1944 px² .

File format: .tif (8-bit).

File naming convention: The file names include the cultivation time (24h or 72h) and the microscope objective lens (10× or 20×), e.g., spec_24h_10x_17.tif.

Dataset subsets:

• Glioma C6-spec: 45 images captured under strictly controlled imaging conditions, divided into training (30 images), validation (4 images), and test (11 images) subsets.
• Glioma C6-gen: 30 images captured under varied imaging and seeding conditions, designed to test model generalization.

Annotations:

• Over 20,000 annotated objects across both subsets, including 12,000 cell annotations and 7,800 soma annotations.
• Glioma C6-spec annotations include Type A cells (spheroid/spindle), Type B cells (flat/spread) and soma (nucleus body).
• Glioma C6-gen subset annotations consist only of general cell instances, without differentiation into types or nuclei.

Article reference:

"Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation," Malashin et al., 2025.

Authors: 

Roman Malashin¹ ², Svetlana Pashkevich³, Daniil Ilyukhin¹, Arseniy Volkov³, Valeria Yachnaya¹ ², Andrey Denisov³, Maria Mikhalkova¹

Affiliation(s):

¹ Pavlov Institute of Physiology, Russian Academy of Science

² Saint-Petersburg State University of Aerospace Instrumentation, Russia

³ Institute of Physiology, NAS of Belarus

Acknowledgments:

The collection and annotation of this dataset were carried out with financial support from the St. Petersburg Science Foundation and the Belarusian Republican Foundation for Fundamental Research (BRFFR, SCST) under the grant “Detection of tumor cells in nervous tissue using deep learning methods” (contract No. M24SPbG010).