Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

Abstract

In the field of cytogenetics, chromosome image analysis or karyotyping from metaphase images plays an imperative role in the diagnosis, prognosis and treatment assessment of different genetic disorders and cancers. This paper is a comprehensive review on different traditional and deep‐based techniques, which are utilized in the design of automated karyotyping systems (AKSs). By this review, a detailed methodology is suggested for the design of end‐to‐end automated karyotyping system (EEAKS) which portrays a sequential multi stage approach. Methods related to all the stages in EEAKS are systematically surveyed by exploring the state of the art literature. Datasets and performance measures incorporated in the past studies are explored. Even though numerous methods were proposed throughout the past three decades, a completely automated framework has not yet been acknowledged. Inferences from this study show that, while various traditional image processing strategies are utilized for pre‐processing and segmentation, machine learning techniques are used only for the classification purpose. In conventional classifiers, artificial neural networks are generally utilized even when the peak performance is given by support vector machines. However, owing to the recent prodigious breakthrough in computer vision, deep neural networks are progressively utilized for developing automated systems. It is seen that deep neural networks are not yet explored in the realm of pre‐processing stage of EEAKS. However, limited number of methods based on convolutional neural networks (CNN) are utilized in all other stages. This review recommends a hybrid CNN for the design of EEAKS, in which all the stages can be automated by sub CNNs. Methodology for generating sufficient datasets is also discussed here which is, indeed, required for further research in this area. This paper concludes with future research directions for the development of a fully automated end‐to‐end karyotyping system.

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

“…Early chromosome instance segmentation was mainly based on statistics and geometry, which adopts threshold, edge, region and other related technologies to achieve better performance for image semantic segmentation [3]. For instance, N.Madian et al [16] propose an algorithm to separate the touching chromosomes and the segmentation of overlapping chromosomes from G-Band metaspread images.…”

“…Any numerical or structural variations of chromosomes may cause disorders such as intellectual disability, congenital malformations, sterility, sexual variations, and even cancers [2]. In clinical practice, karyotype analysis [3] is a routine procedure for the detection and diagnosis of the chromosomal disorders.…”

ChromEDA: Chromosome classification by ensemble framework based domain adaptation

“…It is important to identify chromosomal abnormalities because they may result in gene gain, deletion, or rearrangement, which have a variety of clinical symptoms and clinical repercussions (Chen et al, 2022). The gold standard for chromosome disease prenatal diagnosis is karyotype analysis (Sathyan et al, 2022), which involves staining and imaging the chromosomes by microscopies, then analyzing the characteristics, such as centromere, telomeres, banding patterns, size and shape, and finally, numbering chromosomes to obtain a karyotype map and identify abnormalities. However, traditional karyotyping is mostly done manually by karyotype experts (Al‐Kharraz et al, 2020), which is expensive and labor‐intensive (Moradi & Setarehdan, 2006).…”

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

“…For example, nuclei segmentation in histopathological images is a prerequisite for analyzing the tumor microenvironment [2]. Chromosome segmentation in metaphase cell images is an essential step for karyotyping [3]. As demonstrated in Fig.…”