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

TL;DR

AutoKary2022 introduces a large, densely annotated chromosome dataset to advance instance segmentation methods, enabling better diagnosis of chromosomal disorders through improved image analysis.

Contribution

The paper provides the first large-scale, densely annotated chromosome dataset and systematically evaluates segmentation methods on it, revealing key insights.

Findings

Enhanced understanding of chromosome segmentation challenges

Benchmark results for various segmentation methods

Insights into morphological complexities of chromosomes

Abstract

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.