An Automated System for Chromosome Analysis

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and to provide a basis for statistical analysis of quantitative chromosome measurement data are described

An automated system for chromosome analysis. Volume 1: Goals, system design, and performance

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and a basis for statistical analysis of quantitative chromosome measurement data is described. The prototype was assembled, tested, and evaluated on clinical material and thoroughly documented

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

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.Comment: Accepted by ICME 202

Advances in plant chromosome genomics

AbstractNext generation sequencing (NGS) is revolutionizing genomics and is providing novel insights into genome organization, evolution and function. The number of plant genomes targeted for sequencing is rising. For the moment, however, the acquisition of full genome sequences in large genome species remains difficult, largely because the short reads produced by NGS platforms are inadequate to cope with repeat-rich DNA, which forms a large part of these genomes. The problem of sequence redundancy is compounded in polyploids, which dominate the plant kingdom. An approach to overcoming some of these difficulties is to reduce the full nuclear genome to its individual chromosomes using flow-sorting. The DNA acquired in this way has proven to be suitable for many applications, including PCR-based physical mapping, in situ hybridization, forming DNA arrays, the development of DNA markers, the construction of BAC libraries and positional cloning. Coupling chromosome sorting with NGS offers opportunities for the study of genome organization at the single chromosomal level, for comparative analyses between related species and for the validation of whole genome assemblies. Apart from the primary aim of reducing the complexity of the template, taking a chromosome-based approach enables independent teams to work in parallel, each tasked with the analysis of a different chromosome(s). Given that the number of plant species tractable for chromosome sorting is increasing, the likelihood is that chromosome genomics – the marriage of cytology and genomics – will make a significant contribution to the field of plant genetics

A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes

In clinical diagnosis, karyotyping is carried out to detect genetic disorders due to chromosomal aberrations. Accurate segmentation is crucial in this process that is mostly operated by experts. However, it is time-consuming and labor-intense to segment chromosomes and their overlapping regions. In this research, we look into the automatic segmentation of overlapping pairs of chromosomes. Different from standard semantic segmentation applications that mostly detect object regions or boundaries, this study attempts to predict not only non-overlapping regions but also the order of superposition and opaque regions of the underlying chromosomes. We propose a novel convolutional neural network called Compact Seg-UNet with enhanced deep feature learning capability and training efficacy. To address the issue of unrealistic images in use characterized by overlapping regions of higher color intensities, we propose a novel method to generate more realistic images with opaque overlapping regions. On the segmentation performance of overlapping chromosomes for this new dataset, our Compact Seg-UNet model achieves an average IOU score of 93.44% ± 0.26 which is significantly higher than the result of a simplified U-Net reported by literature by around 6.08%. The corresponding F1 score also increases from 0.9262 ± 0.1188 to 0.9596 ± 0.0814

Recent Trends in Cytogenetic Studies

Recent Trends in Cytogenetic Studies - Methodologies and Applications deals with recent trends in cytogenetics with minute details of methodologies that can be adopted in clinical laboratories. The chapters deal with basic methods of primary cultures, cell lines and their applications; microtechnologies and automations; array CGH for the diagnosis of fetal conditions; approaches to acute lymphoblastic and myeloblastic leukemias in patients and survivors of atomic bomb exposure; use of digital image technology and using chromosomes as tools to discover biodiversity. While concentrating on the advanced methodologies in cytogenetic studies and their applications, authors have pointed out the need to develop cytogenetic labs with modern tools to facilitate precise and effective diagnosis to benefit the patient population