Crowdsourcing for Chromosome Segmentation and Deep Classification

Sharma, Monika; Saha, Oindrila; Sriraman, Anand; Hebbalaguppe, Ramya; Vig, Lovekesh; Karande, Shirish

doi:10.1109/cvprw.2017.109

Cited by 77 publications

(75 citation statements)

References 13 publications

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“…Charters and Graham [4] provided an algorithm to segment according to the comparison between the band profiles and the templates. Sharma et al [1] used crowdsourcing to segment out the chromosomes and represented a method to evaluate the results. Many approaches based on the heuristic rules and geometry are also represented, such as [5]- [7].…”

Section: A Chromosome Segmentationmentioning

confidence: 99%

“…Annotation in pixel-level is daunting for microscopic images like the metaphase chromosome image. The widely used method to collect annotated data is crowdsourcing [1]; however, the quality of these crowdsourcing data is hard to ensure, because the annotation of medical images is a tough task and requires operators to have a wealth of experience. As the advent of generative adversarial networks (GAN), many synthesis methods based on GAN had been developed [15], [16].…”

Section: A Chromosome Segmentationmentioning

confidence: 99%

“…Then, many CNN-based methods on chromosome classification have been developed. Sharma et al [1] first used CNN to classify chromosome. In this method, the preprocessing procedure was also proposed to straighten those curved chromosomes.…”

Section: B Chromosome Classificationmentioning

confidence: 99%

“…In clinic, the chromosomes are often curved or bent heavily, which makes it harder to verify the classes of chromosomes. To minimize the human effort consumption in karyotype analysis, Sharma et al [1] provided a chromosome segmentation method based on crowdsourcing. This is the first who used CNN on chromosome classification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Xie

Liu

et al. 2019

IEEE Access

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Karyotype analysis is one of the main techniques of cytogenetics through medical image processing, which has an important role in modern medical treatment and diagnosis. The process of human karyotype analysis contains two key components: Firstly, chromosomes are segmented from metaphase chromosome digital images taken under a microscope. Chromosomes then are analyzed, compared, ordered and classified one by one carefully. Under this procedure, the operation on segmentation and classification is cumbersomely time-consuming, where traditional geometric or statistical methods only have limited effect due to low accuracy. Thus, in most conditions, human effort is still heavily required to monitor the workflow and correct the errors. In this paper, we present an integrated workflow to segment out and classify chromosomes automatically using a combination of multiple input convolutional neural networks (CNN) and geometric optimization, called mCNN_GO. We investigate Mask R-CNN to segment out chromosomes from metaphase chromosome images and train the mCNN_GO to classify the sub-images. To improve the performance of the segmentation network, we adapt a new feature-based approach to synthesize images on the labeled data. Furthermore, we develop a geometric algorithm to straighten the chromosomes before classification to ensure the consistency of the data. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods on automatic karyotype analysis. INDEX TERMS Biomedical image processing, image processing, machine learning.

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Section: A Chromosome Segmentationmentioning

confidence: 99%

Section: A Chromosome Segmentationmentioning

confidence: 99%

Section: B Chromosome Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Karyotype Analysis Using CNN and Geometric Optimization

Xie

Liu

et al. 2019

IEEE Access

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“…Ehteshami Bejnordi showed the excellent performance of an improved CNN in lymph node metastasis detection [9]. Monika [10] used a method combining the crowdsourcing, preprocessing and deep learning to segment out and classify chromosomes especially with overlapping chromosomes. The accuracy of classification was 86.7%.…”

Section: Related Workmentioning

confidence: 99%

Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network

Yan¹,

Tucci²,

Jaffe³

2019

JCC

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Karyotype analysis has significant clinical importance. Effectively detecting the exact abnormity of chromosomes will contribute to the diagnosis of certain diseases. In this paper, I presented a convenient and reliable system that was capable of detecting t(9;22) chromosome translocation, a specific chromosomal abnormity in CML patients. The functions of this system were based on deep learning algorithms, and I created a classification system using ResNet. The model could effectively detect t(9;22) translocation based on images of chromosomes 9 and 22. This model achieves a 97.5% accuracy on the validation set.

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Karyotyping of human chromosomes in metaphase images using faster R‐CNN and inception models

Chavan,

Nair,

Nimbalkar

et al. 2024

Int J Imaging Syst Tech

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Karyotyping is the process of pairing and ordering human chromosomes from metaphase chromosomal images depending on their size, centromere position, and banding patterns. It is used to analyze human chromosomes for various genetic disorders especially during prenatal screenings. Since manual karyotyping is a labor‐intensive and a time‐consuming task, developing an automatic or semi‐automatic computer‐assisted karyotyping system is the need of the hour. The proposed karyotyping system aims to detect human chromosomes (22 pairs of autosomes and one pair of sex chromosome) from the microscopic metaphase images which is followed by separation of overlapped chromosomes. Extracted chromosomes are first classified into seven Denver groups (A to G) followed by classifying individual chromosomes within their Denver group. Then the classified chromosome pairs are used to create a karyotype with the assistance of cytologists or by using estimated chromosome length as cytogenetic parameter. A total of 234 chromosomal images from two different public datasets are used for experimentation. Human chromosomes in the microscopic metaphase images are detected using faster regions with convolutional neural networks (Faster R‐CNN) combined with Inception v2. Then convexity defect algorithm is preferred for separation of overlapped chromosomes. The detected chromosomes are classified using the proposed two‐step approach in which Inception v3 model is used. Then the classified chromosome pairs are used to create a karyotype with the assistance of cytologists or by using estimated chromosome length as cytogenetic parameter. Faster R‐CNN model gives a detection accuracy of 98.53%. Denver group classification with the two‐step approach provides a better accuracy of 84.59% when network is trained for 64 902 epochs. Faster R‐CNN outperforms in detection and works faster as it searches chromosomes within the regional proposals. The two‐step classification approach gives better classification accuracy. The proposed approach also works very well for overlapping chromosomes due to use of convexity defects algorithm. The classified chromosome pairs are useful tools for the cytologists to create a karyotype with minimal efforts.

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Crowdsourcing for Chromosome Segmentation and Deep Classification

Cited by 77 publications

References 13 publications

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network

Karyotyping of human chromosomes in metaphase images using faster R‐CNN and inception models

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