Automated Karyotyping of Metaphase Cells with Touching Chromosomes

Munot, Mousami V.; Joshi, Manjusha; Sharma, Nikhil

doi:10.5120/3700-5175

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…In addition to this, a complete automated solution for the separation of overlapped chromosomes is not yet designed due to its intrinsic varying complexity driven by number and geometry of occlusions. Most of the segmentation algorithms are limited to less number of overlaps or touching with simpler structures (Charters & Graham, 2002; Munot et al, 2011; Moallem et al, 2013, Somasundaram & Kumar, 2014a). In some cases small sized occluded chromosomes are not even identified for their segmentation Grisan et al (2009).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…The proposed algorithm to identify the cut-points, assumes that the cluster of overlapping chromosomes is already segmented from the metaphase image e.g. by the approach detailed in one of our earlier work reported in [9]. For the sake of comprehensiveness, fig.…”

Section: Proposed Approachmentioning

confidence: 99%

“…For the sake of comprehensiveness, fig. 1 depicts the intermediate results of the algorithm reported in [9] to segment a cluster with two overlapping chromosomes in a metaphase image of the database, used for the proposed experimentation. The proposed approach to detect the cut points is based on valid postulations based on the computational geometry of the boundary pixels.…”

Section: Proposed Approachmentioning

confidence: 99%

Automated detection of cut-points for disentangling overlapping chromosomes

Munot¹,

Joshi²,

Sharma

et al. 2013

2013 IEEE Point-of-Care Healthcare Technologies (PHT)

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Automating chromosome classification and segmentation has been a major challenge in automated karyotyping especially due to overlapping chromosomes. The earlier reported methods for disentangling the chromosome overlaps have limited success as they are sensitive to scale variations, computationally complex, use only color information in case of multispectral imaging and most of them are limited to separation of single overlap of two chromosomes in a cluster. This work proposes a heuristic approach to locate the cut-points on an overlap and thus addresses the first step towards automated extrication of overlapping chromosomes from the metaphase image. The proposed simple, but novel and robust algorithm, to identify the appropriate cut points is based on computational geometry of the pixels on the boundary of the cluster. Contribution of this work is in the ability of the algorithm to successfully identify the correct cut points in multiple overlaps with multiple chromosomes. Moreover, as the proposed algorithm is independent of shape and color information, it also applies to Multicolour Fluorescence in-situ Hybridization (MFISH) metaphase chromosomes, System performance was tested and analyzed using 40 real images giving an overall average accuracy of 98% for correct identification of cut points in 2, 3 and 4 overlapping chromosomes.

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“…In the case of pteridophytes and bryophytes, karyotype studies are still a pending task and the scarce studies available to date could be a consequence of the presence of large chromosome numbers and, in many cases, the existence of small chromosomes which make measurements difficult. Nevertheless, the use of computerized methods for image analysis can potentially facilitate studies in the near future such as those performed in various plant groups many years ago (Bauchan and Hossain, 2001;Munot et al, 2011).…”

Section: Karyotypesmentioning

confidence: 99%

Plant and Animal Cytogenetics in Latin America

2022

BAG

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Chile is located in the south-western region of South America along the Pacific Ocean and contributes to the worldwide flora with ca. 6,120 species of Bryophyta, Pteridophyta, Pinophyta, Gnetophyta, and Magnoliophyta (1.9% of worldwide total species), exhibiting high endemism across all plant divisions. Little is known about the genetic diversity of Chilean land plants worldwide, including their cytogenetic and molecular characteristics. In 2012 we published the first state-of-the-art review in Cytogenetics of Chilean Angiosperms. The article gathered 78 publications from 1924 to 2010 accounting for approximately 139 species (2.8% of total Chilean species). The aim of this paper was to review the advances in cytogenetic studies of Chilean land plants, reporting additional cytogenetic data for species of four botanical divisions until 2020.Cytogenetic data were searched in the CPCD (Chilean Plants Cytogenetic Database). In total, we found 180 publications from both Chilean and foreign researchers. To date, cytogenetic data have been reported for 499 Chilean land plant species (8.2% of total) belonging to 244 genera and 117 families. In this context, the 2001-2020 period has been among the most productive regarding publications, with 74 available reports that include 163 additional species. Based on chromosome numbers, angiosperms and bryophytes registered the greatest diversity with 55 and 29 different 2n, respectively; both divisions having the greatest number of studied species. Given the importance of increasing information on Chilean land plants, it is expected that more publications will contribute to the knowledge of their cytogenetic diversity in the near future.

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Automated Karyotyping of Metaphase Cells with Touching Chromosomes

Cited by 12 publications

References 16 publications

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

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

Automated detection of cut-points for disentangling overlapping chromosomes

Plant and Animal Cytogenetics in Latin America

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