# AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation

arXiv: https://arxiv.org/abs/2303.15839 · HTML: https://mlsanity.com/paper/2303.15839/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [SOLOv2: Dynamic and Fast Instance Segmentation](https://mlsanity.com/paper/2003.10152/) (2020)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification](https://mlsanity.com/paper/2109.10498/) (2021)

## Built on by (most cited first, 5 of 5)

- [Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation](https://mlsanity.com/paper/2312.17030/) (2023)
- [FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation](https://mlsanity.com/paper/2310.10951/) (2023)
- [Supervised Contrastive Learning for Fine-grained Chromosome Recognition](https://mlsanity.com/paper/2312.07623/) (2023)
- [An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology](https://mlsanity.com/paper/2505.15868/) (2025)
- [Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy](https://mlsanity.com/paper/2401.09895/) (2024)

## Abstract (arXiv)

Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i.e., karyotype analysis). However, it is still a challenging task due to lacking of densely annotated datasets and the complicated morphologies of chromosomes, e.g., dense distribution, arbitrary orientations, and wide range of lengths. To facilitate the development of this area, we take a big step forward and manually construct a large-scale densely annotated dataset named AutoKary2022, which contains over 27,000 chromosome instances in 612 microscopic images from 50 patients. Specifically, each instance is annotated with a polygonal mask and a class label to assist in precise chromosome detection and segmentation. On top of it, we systematically investigate representative methods on this dataset and obtain a number of interesting findings, which helps us have a deeper understanding of the fundamental problems in chromosome instance segmentation. We hope this dataset could advance research towards medical understanding. The dataset can be available at: https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset.