Preprocessing G-banded metaphase: towards the design of automated karyotyping

Remya, R.; Hariharan, S; Keerthi, V.; Gopakumar, C.

doi:10.1007/s42452-019-1754-z

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Using the search terms (Machine learning or Deep learning), (Chromosomal disease or Karyotyping or genetic disorder), the papers and articles were chosen. [19–40]. Additionally, articles were chosen based on the exclusion and inclusion criteria.…”

Section: Methodsmentioning

confidence: 99%

“…The CIR-contrast improvement ratio, a quantitative metric, is developed to estimate the improved results after comparing an algorithm to numerous existing image-enhancing algorithms. The investigated results predicted from the proposed method are consistently better than the current stage regarding visual effect and the CIR measure [29].…”

Section: Literature Surveymentioning

confidence: 99%

“…Heuristic search edge-linking algorithms seek to separate contacting chromosomes from one another by searching for a least-the cost-connected path that divides the chromosomes. The lost link of such methods to the specific issue of chromosomal separation yields poor results when there is inadequate information, such as when a separating path is absent [40].…”

Section: Literature Surveymentioning

confidence: 99%

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A study of machine learning techniques for Automated Karyotyping System

Kaur,

Dhir

2023

Preprint

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Genetic abnormalities constitute a considerable share of all the existing societal healthcare issues. There has been a dire need for the automation of chromosomal analysis, hence supporting laboratory workers in effective classification and identifying such abnormalities. Nevertheless, with many modern image processing techniques, like Karyotyping, improved the life expectancy and the quality of life of such cases. The standard image-based analysis procedures include Pre-processing, Segmentation, Feature extraction, and Classification of images. When explicitly considering Karyotyping, the processes of Segmentation and Classification of chromosomes have been the most complex, with much existing literature focusing on the same. Various model-based machine learning models have proven to be highly effective in solving existing issues and building an artificial intelligence-based, autonomous-centric karyotyping system. An autonomous Karyotyping System will connect the pre-processing, Segmentation, and classification of metaphase images. The review focuses on machine learning-based algorithms for efficient classification accuracy. The study has the sole motive of moving towards an effective classification method for karyotype metaphase images, which will eventually predict the fetus’s abnormalities more effectively. The study’s results shall benefit future researchers working in this area.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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A study of machine learning techniques for Automated Karyotyping System

Kaur,

Dhir

2023

Preprint

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“…Also, the quality of captured images may be degraded due to sensor noise and noises due to staining. Conventional smoothening and sharpening filters like Gaussian filters or median filters are utilized for denoising metaphase images (Keerthi et al (2016); Remya et al, 2019). Efficiency of automated karyotyping may diminish if the input image contains interphase cells, stain and other undesirable obstructions.…”

Section: Traditional Techniques For Aks Designmentioning

confidence: 99%

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

et al. 2021

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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.

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“…Nonetheless, staining debris removal is underexposed. As discussed by Mousami, 3 Remya et al, 4 Xingwei et al, 5 traditional denoising filters, such as Gaussian, bilateral, and median filters are only used for suppressing noise in the metaphase images. Methods for the detection and removal of interphase cells are explored by very few researchers.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

Sathyan

Menon

Prasad³

et al. 2022

Int J Imaging Syst Tech

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Segmentation plays an essential role in the design of the automated karyotyping system (AKS). It is pivotal to segment interphase cells and other debris usually found in the input G metaphase images. The performance of AKSs is considerably less when interphase cells and debris are present in the input images. In this article, two semantic segmentation models are proposed. For this experiment, an annotated dataset is generated from the G banded metaphase images which are prepared at Regional Cancer Centre (RCC), Thiruvananthapuram, Kerala, India. Inspired by the results of UNet, a lighter version L‐UNet is developed and experimented with. It shows the validation IoU (Intersection over Union) of 0.9809 and F1‐score of 0.9903 on the RCC dataset and the test IoU of 0.9720 and F1‐score of 0.9858 on the CRCN‐NE dataset. As backbone semantic segmentation models are state of the art, an efficient model, Eff‐UNet, is also proposed here. In this model, EfficientNetB03 acts as the backbone that extracts powerful features and UNet acts as the decoder that predicts the segmentation map. It performs with the validation IoU of 0.9842 and F1‐score of 0.9920 on the RCC dataset and the test IoU of 0.7545 and F1‐score of 0.7778 on the CRCN‐NE dataset. To derive this model, 25 encoder–decoder architectures are evaluated with various top‐performing CNNs (convolutional neural networks) as encoders and segmentation networks as decoders. Results are further compared with various segmentation models and the best results are obtained from the proposed model.

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Preprocessing G-banded metaphase: towards the design of automated karyotyping

Cited by 9 publications

References 22 publications

A study of machine learning techniques for Automated Karyotyping System

A study of machine learning techniques for Automated Karyotyping System

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

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

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