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

2025-11-10

AI Summary

High-quality annotated data for glioma C6 cell instance segmentation is scarce, hindering robust model development and evaluation. Method: We introduce C6Seg—the first open-source, biologist-curated instance segmentation dataset for C6 cells—comprising 75 phase-contrast microscopy images with over 12,000 pixel-accurate cell masks. C6Seg uniquely incorporates morphological classification labels and subcellular annotations (soma vs. pseudopodia), and spans controlled conditions and multi-condition imaging environments to enhance generalizability. Contribution/Results: Using C6Seg, we systematically benchmark state-of-the-art models (e.g., Mask R-CNN, U-Net), revealing performance bottlenecks in highly clustered and small-object scenarios. Transfer learning on C6Seg yields an 8.2% mAP improvement, validating its utility for model refinement and benchmark establishment. C6Seg thus provides a reproducible, high-fidelity resource for quantitative analysis of brain tumor cells.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Provides benchmark dataset for glioma cell instance segmentation
• Addresses limitations of existing models on biomedical images
• Enables morphological categorization to advance cancer research

Innovation

• Glioma C6 dataset for cell segmentation training
• Includes high-resolution images with annotated cells
• Supports benchmarking and generalization testing models