An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification
Jenn-Jhy Tseng, Chien‐Hsing Lu, Junzhou Li, Hui-Yu Lai, Minhu Chen, Fu‐Yuan Cheng, Chih‐En Kuo
Abstract
Chromosomes are a principal target of clinical cytogenetic studies. While chromosomal analysis is an integral part of prenatal care, the conventional manual identification of chromosomes in images is time-consuming and costly. This study developed a chromosome detector that uses deep learning and that achieved an accuracy of 98.88% in chromosomal identification. Specifically, we compiled and made available a large and publicly accessible database containing chromosome images and annotations for training chromosome detectors. The database contains five thousand 24 chromosome class annotations and 2,000 single chromosome annotations. This database also contains examples of chromosome variations. Our database provides a reference for researchers in this field and may help expedite the development of clinical applications.