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

arXiv: https://arxiv.org/abs/2511.07286 · HTML: https://mlsanity.com/paper/2511.07286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting](https://mlsanity.com/paper/2309.06824/) (2023)
- [MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy](https://mlsanity.com/paper/2212.03465/) (2022)
- [CellSeg1: Robust Cell Segmentation with One Training Image](https://mlsanity.com/paper/2412.01410/) (2024)
- [Trans2Unet: Neural fusion for Nuclei Semantic Segmentation](https://mlsanity.com/paper/2407.17181/) (2024)
- [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images](https://mlsanity.com/paper/2310.14226/) (2023)
- [Sketchpose: Learning to Segment Cells with Partial Annotations](https://mlsanity.com/paper/2508.17798/) (2025)

## Built on by (most cited first, 1 of 1)

- [Self-supervised Pretraining of Cell Segmentation Models](https://mlsanity.com/paper/2604.10609/) (2026)

## Abstract (arXiv)

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.