Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation

• URL: http://arxiv.org/abs/2511.07286v1
• Date: Mon, 10 Nov 2025 16:33:34 GMT
• Title: Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation
• Authors: Roman Malashin, Svetlana Pashkevich, Daniil Ilyukhin, Arseniy Volkov, Valeria Yachnaya, Andrey Denisov, Maria Mikhalkova,
• Abstract summary: We present Glioma C6, a new open dataset for instance segmentation of glioma C6 cells.<n>The dataset comprises 75 high-resolution phase-contrast microscopy images with over 12,000 annotated cells.<n>It includes soma annotations and morphological cell categorization provided by biologists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Glioma C6, a new open dataset for instance segmentation of glioma C6 cells, designed as both a benchmark and a training resource for deep learning models. The dataset comprises 75 high-resolution phase-contrast microscopy images with over 12,000 annotated cells, providing a realistic testbed for biomedical image analysis. It includes soma annotations and morphological cell categorization provided by biologists. Additional categorization of cells, based on morphology, aims to enhance the utilization of image data for cancer cell research. Glioma C6 consists of two parts: the first is curated with controlled parameters for benchmarking, while the second supports generalization testing under varying conditions. We evaluate the performance of several generalist segmentation models, highlighting their limitations on our dataset. Our experiments demonstrate that training on Glioma C6 significantly enhances segmentation performance, reinforcing its value for developing robust and generalizable models. The dataset is publicly available for researchers.

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