Adaptive Segmentation of DAPI-stained, C-banded, Aggregated and Overlapping Chromosomes.
Journal
Cell biochemistry and biophysics
ISSN: 1559-0283
Titre abrégé: Cell Biochem Biophys
Pays: United States
ID NLM: 9701934
Informations de publication
Date de publication:
04 Aug 2024
04 Aug 2024
Historique:
accepted:
19
07
2024
medline:
4
8
2024
pubmed:
4
8
2024
entrez:
4
8
2024
Statut:
aheadofprint
Résumé
Existing algorithms for automated segmentation of chromosomes and centromeres do not work well for condensed, C-banded and DAPI-stained chromosomes and centromeres. Overlapping and aggregation, which frequently occur in metaphase spreads, introduce additional challenges to the counting of chromosomes and centromeres in the Dicentrics Chromosome Assay (DCA). In this paper, we introduce adaptive algorithms, for segmentation of difficult metaphase spreads that include overlapping and aggregated chromosomes. In order to enhance and segment chromosomes, two optimizations are done: (1) the best algorithm among several options is automatically chosen based on predefined figures of merit, (2) the algorithm is automatically optimized with a binary search to modify its parameters to achieve predefined thresholds. These algorithms are designed to separate mildly or moderately aggregated chromosomal clusters. The clusters are segmented by skeleton junctions, reduction of the overall object thickness, and the watershed algorithm. The chromosomes are characterized by rules we establish, using minimal assumptions. Centromeres are detected by detecting bright spots on the surface of the chromosomes, and then using cluster analysis and shape and intensity profiles to identify them as centromeres. High sensitivity and specificity for chromosome and centromere detection were achieved.
Identifiants
pubmed: 39097855
doi: 10.1007/s12013-024-01453-z
pii: 10.1007/s12013-024-01453-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Cytogenetic dosimetry applications in preparedness for and response to radiation emergencies, in EPR-Biodose, IAEA, Vienna, 2011.
IAEA-EPR. Cytogenetic Dosimetry: Applications in Preparedness for, and Response to Radiation Emergencies. IAEA, Vienna 2011, Section 9.1.5, 9.1.6, 9.1.2: 55–58.
TM8-125, Nuclear Handbook for Medical Service Personnel, US Army 1969.
Prasanna, P. G., Moroni, M., & Pellmar, T. C. (2010). Triage dose assessment for partial-body exposure: dicentric analysis. Health Physics, 98(2), 244–251.
doi: 10.1097/01.HP.0000348020.14969.4
pubmed: 20065689
pmcid: 2806648
High Dose Radiation Effects and Tissue Injury, Report of the Independent Advisory Group on Ionizing Radiation. RCE-10, Documents of the Health Protection Agency, Radiation, Chemical and Environmental Hazards, March 2009.
Gonen, R., Platkov, M., Gardos, Z., Shayir, S., Levitsky, I., Weinstein, M., & Manor, E. (2022). A DAPI-Based Modified C-banding Technique for a Rapid Achieving High Photographic Contrast of Centromeres on Chromosomes. Cell Biochemistry & Biophysics, 80(2), 375–384.
doi: 10.1007/s12013-022-01065-5
https://metasystems-international.com/en/products/metafer/ .
Furukawa, A., Minamihisamatsu, M., & Hayata, I. (2010). Low-cost metaphase finder system. Health Physics, 98(2), 269–275. (and refs therein).
doi: 10.1097/HP.0b013e3181b357c1
pubmed: 20065693
M’kacher, R., Maalouf, E. E., Ricoul, M., Heidingsfelder, L., Laplagne, E., Cuceu, C., Hempel, W. M., Colicchio, B., Dieterlen, A., & Sabatier, L. (2014). New tool for biological dosimetry: Reevaluation and automation of thegold standard method following telomere and centromere staining. Mutation Research, 770, 45–53.
doi: 10.1016/j.mrfmmm.2014.09.007
pubmed: 25771869
Thiago, S. F., Lloyd, D., & Amaral, A. (2008). A comparison of different cytological stains for biological dosimetry. International Journal of Radiation Biology, 84, 703–711.
doi: 10.1080/09553000802241770
Nakata, A., Akiyama, M., Yamada, Y., & Yoshida, M. A. (2011). Modified c-band technique for the analysis of chromosome abnornalities in irradiated human lymphocytes. Radiation Measurements, 46, 1113–1116.
doi: 10.1016/j.radmeas.2011.07.037
Jeong, S. K., Oh, S. J., Kim, S. H., Jang, S., Kang, Y. R., Kim, H., Kye, Y. U., Lee, S. H., Lee, C. G., Park, M. T., Kim, J. S., Jeong, M. H., & Jo, W. S. (2022). Dicentric chromosome assay using a deep learning-based automated system. Science Reports, 12(1), 22097.
doi: 10.1038/s41598-022-25856-1
Wadhwa, A. S., Tyagi, N., & Chowdhury, P. R. (2022). Deep Learning based Automatic Detection of Dicentric Chromosome. arXiv, 2204, 08029.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv, 1505, 04597.
Banerjee, S., Magee, L., Wang, D., Li, X., Huo, B.-X., Jayakumar, J., Matho, K., Lin, M.-K., Ram, K., & Sivaprakasam, M., et al. (2020). Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder–decoder deep networks. Nature Machine Intelligence, 2, 585–594.
doi: 10.1038/s42256-020-0227-9
pubmed: 34604701
pmcid: 8486300
Wang C. Y., Bochkovskiy A., Liao H. Y. M. Scaled-YOLOv4: Scaling Cross Stage Partial Network. CoRR 2020 abs/2011.0.
Shuryak, I., Royba, E., Repin, M., Turner, H. C., Garty, G., Deoli, N., & Brenner, D. J. (2022). A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays. Science Reports, 12(1), 21077.
doi: 10.1038/s41598-022-25453-2
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing System, 30, 3146–3154.
Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of Big Data, 7, 94.
doi: 10.1186/s40537-020-00369-8
pubmed: 33169094
pmcid: 7610170
Jang, S., Shin, S. G., Lee, M. J., Han, S., Choi, C. H., Kim, S., Cho, W. S., Kim, S. H., Kang, Y. R., Jo, W., Jeong, S., & Oh, S. (2021). Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay. Radiation Research, 195(2), 163–172.
pubmed: 33316052
Shen, W., Bai, X., Hu, R., Wang, H., & Latecki, L. J. (2011). Skeleton growing and pruning with bending potential ratio. Pattern Recognition, 44, 196–209.
doi: 10.1016/j.patcog.2010.08.021
Shen, X., Qi, Y., Ma, T., & Zhou, Z. (2019). Adicentric chromosome identification method based on clustering and watershed algorithm. Science Report, 9, 1–11.
de Faria, E. R., Guliato, D., de Sousa Santos, J. C. Segmentation and Centromere Locating Methods Applied to Fish Chromosomes Images. In: Setubal, J.C., Verjovski-Almeida, S. (eds) Advances in Bioinformatics and Computational Biology 2005; Lecture Notes in Computer Science, vol 3594. Springer, Berlin, Heidelberg: 181–189.
Varghese, J., Subash, S., & Tairan, N. (2016). Fourier transform-based windowed adaptive switching minimum filter for reducing periodic noise from digital images. IET Image Processing, 10(9), 646–656.
doi: 10.1049/iet-ipr.2015.0750
Liu, J., Li, Y., Wilkins, R., Flegal, F., Knoll, J. H. M., & Rogan, P. K. (2017). Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection. F1000Research, 6, 1396.
doi: 10.12688/f1000research.12226.1
pubmed: 29026522
pmcid: 5583746
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transaction of System, Man & Cybernetics, 9, 62–66.
doi: 10.1109/TSMC.1979.4310076
Manohar R., Gawande J. Watershed and Clustering Based Segmentation of Chromosome Images. IEEE 7th International Advance Computing Conference (IACC). 2017; 697–700.
Karvelis, P. S., Tzallas, A. T., Fotiadis, D. I., & Georgiou, I. (2008). A multichannel watershed-based segmentation method for multispectral chromosome classification. IEEE Transaction on Medical Imaging, 27, 697–708.
doi: 10.1109/TMI.2008.916962
Yan W., Shen S. An edge detection method for chromosome images, in: 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. 2008; 2390–2392.
Subasinghe, A., Samarabandu, J., Li, Y., Wilkins, R., Flegal, F., Knoll, J. H. M., & Rogan, P. K. (2016). “Centromere detection of human metaphase chromosome images using a candidate based method”. F1000Research, 5, 1565.
doi: 10.12688/f1000research.9075.1
Mahmoud, A. M., & Masatoki, T. (2012). Cy tological Karyotyping and Characterization of a 410 Kb Mini-Chromosome in Nectria Haematococca MPI. Mycologia, 104(4), 845–856.
doi: 10.3852/11-306
pubmed: 22453120
Joshi, I., Kumar Mondal, A., & Navab, N. (2023). Chromosome Cluster Type Identification Using a Swin Transformer. Applied Science, 13(14), 8007.
doi: 10.3390/app13148007
Sathyan, R. R., Menon, G. C., Hariharan, S., Thampi, R. K., & Duraisamy, J. H. (2021). Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review. Expert Systems, 39(3), e12799.
doi: 10.1111/exsy.12799
Al-Ameri, H. A., & Al-Hameed, W. (2020). New algorithm for separation overlapping & touching chromosomes. Journal of Physics: Conference Series, 1530, 012024.
Minaee, S., Fotouhi, M., & Khalaj, B. H. A geometric approach to fully automatic chromosome segmentation. IEEE Signal Processing in Medicine and Biology Symposium (SPMB), arxiv 2014;1112.4164.
Joshi Mu.A., Munot M. V., Joshi Ma.A., Shah K. R., Soni K., Automated Detection of the Cut-points for the Separation of Overlapping Chromosomes. IEEE EMBS International Conference on Biomedical Engineering and Sciences, Langkawi, 17th - 19th December 2012: 820-825.
Yilmaz, I. C., Yang, J., Altinsoy, E., & Zhou, L. An Improved Segmentation for Raw G-Band Chromosome Images. 5th International Conference on Systems and Informatics (ICSAI) Nanjing 2018: 944-950.
Poletti, E., Zappelli, F., Ruggeri, A., & Grisan, E. (2012). “A review of thresholding strategies applied to human chromosome segmentation”. Computer Methods and Programs in Biomedicine, 108(2), 679–688.
doi: 10.1016/j.cmpb.2011.12.003
pubmed: 22261220
Liu J., Li Y., Wilkins R., Flegal F., Knoll J. H., Rogan P. K., Accurate cytogenetic biodosimetry through automation of dicentric chromosome curation and metaphase cell selection. bioRxiv 120410, 2017, F1000Res. 6:1396, 2017
Mahmoud, A. M., & Masatoki, T. (2012). Cytological Karyotyping and Characterization of a 410 Kb Mini-Chromosome in Nectria Haematococca MPI. Mycologia, 104(4), 845–856.
doi: 10.3852/11-306
pubmed: 22453120
Beaton-Green, L. A., Wilkins, R. C. Quantitation of Chromosome Damage by Imaging Flow Cytometry. In: Barteneva, N., Vorobjev, I. (eds) Imaging Flow Cytometry. Methods in Molecular Biology, 2016, vol. 1389. Humana Press, New York, NY, 2016. ISBN 978-1-4939-3302-0_6.
Li, Y., Shirley, B. C., Wilkins, R. C., Norton, F., Knoll, J. H. M., & Rogan, P. K. (2019). Radiation Dose Estimation by Completely Automated Interpretation of the Dicentric Chromosome Assay. Radiation Protection Dosimetry, 186(1), 42–47.
pubmed: 30624749
Shirley, B., Li, Y., Knoll, J. H. M., & Rogan, P. K. (2017). Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation. Journal of Visualised Experiments, 127, 56245.
Platkov, M., Hadad, U., Burg, A., Levitsky, I., Zagatzki, M., Damri, O., Weiss, A., Lauber, Y., Amar, S., Carmel, L., & Gonen, R. (2022). Metaphase Cells Enrichment for Efficient Use in the Dicentric Chromosome Assay. Cell Biochemistry & Biophysics, 80(4), 647–656.
doi: 10.1007/s12013-022-01106-z
https://imagej.net/software/fiji/downloads .
Lewis, G. N., Boynton, N. J., & Burton, F. W. (1981). Expected Complexity of Fast Search with Uniformly Distributed Data. Information Processing Letters, 13, 4–7.
doi: 10.1016/0020-0190(81)90140-X
Knuth, D., “Sorting and searching” in The Art of Computer Programming”, vol.3, 2
Royba, E., Repin, M., Pampou, S., Karan, C., Brenner, D. J., & Garty, G. (2019). “RABiT-II-DCA: A Fully-automated Dicentric Chromosome Assay in Multiwell Plate. Radiation Research, 192(3), 311–323.
doi: 10.1667/RR15266.1
pubmed: 31295087
pmcid: 8567107
https://imagej.nih.gov/ij/plugins/versatile-wand-tool/index.html .
Kakui, Y., Barrington, C., Kusano, Y., Thadani, R., Fallesen, T., Hirota, T., & Uhlmann, F. (2022). Chromosome arm length, and a species-specific determinant, define chromosome arm width. Cell Reports, 41, 111753.
doi: 10.1016/j.celrep.2022.111753
pubmed: 36476849
Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.
doi: 10.1007/BF02294245
Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006, ISBN: 978-0387310732.
Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. in IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650–1654.
doi: 10.1109/TPAMI.2002.1114856