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

TL;DR

Glioma C6 is a new open dataset with high-resolution microscopy images and detailed annotations, designed to improve and benchmark deep learning models for glioma cell segmentation.

Contribution

The paper introduces Glioma C6, a comprehensive dataset with annotations and morphological categorizations, serving as a benchmark and training resource for biomedical image segmentation.

Findings

Training on Glioma C6 improves segmentation accuracy.

Models show limitations when tested on this dataset.

The dataset supports generalization testing under various conditions.

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.