Acknowledgement
This research was supported by the grant of the Korea Institute of Radiological and Medical Sciences, funded by the Ministry of Science and ICT (No. 50445-2024)
References
- IAEA, Cytogenetic Dosimetry: Applications in Preparedness for and Response to Radiation Emergencies, International Atomic Energy Agency, 2011.
- C. Schunck, T. Johannes, D. Varga, T. Lorch, A. Plesch, New developments in automated cytogenetic imaging: unattended scoring of dicentric chromosomes, micronuclei, single cell gel electrophoresis, and fluorescence signals, Cytogenet. Genome Res. 104 (1-4) (2004) 383-389.
- P.K. Rogan, Y. Li, A. Wickramasinghe, A. Subasinghe, N. Caminsky, W. Khan, J. Samarabandu, R. Wilkins, F. Flegal, J.H. Knoll, Automating dicentric chromosome detection from cytogenetic biodosimetry data, Radiat Prot Dosimetry 159 (1-4) (2014) 95-104.
- B. Shirley, Y. Li, J.H.M. Knoll, P.K. Rogan, Expedited radiation biodosimetry by automated dicentric chromosome identification (ADCI) and dose estimation, J. Vis. Exp. 127 (2017).
- J. Liu, Y. Li, R. Wilkins, F. Flegal, J.H.M. Knoll, P.K. Rogan, Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection, F1000Research 6 (2017) 1396.
- E. Royba, M. Repin, S. Pampou, C. Karan, D.J. Brenner, G. Garty, RABiT-II-DCA: a Fully-automated dicentric chromosome assay in Multiwell Plates, Radiat. Res. 192 (3) (2019) 311-323.
- X. Shen, Y. Qi, T. Ma, Z. Zhou, A dicentric chromosome identification method based on clustering and watershed algorithm, Sci. Rep. 9 (1) (2019) 2285.
- S. Jang, S.G. Shin, M.J. Lee, S. Han, C.H. Choi, S. Kim, W.S. Cho, S.H. Kim, Y. R. Kang, W. Jo, S. Jeong, S. Oh, Feasibility study on automatic Interpretation of radiation dose using deep learning technique for dicentric chromosome assay, Radiat. Res. 195 (2) (2021) 163-172.
- Y. Li, B.C. Shirley, R.C. Wilkins, F. Norton, J.H.M. Knoll, P.K. Rogan, Radiation dose estimation by completely automated Interpretation of the dicentric chromosome assay, Radiat Prot Dosimetry 186 (1) (2019) 42-47.
- X. Shen, T. Ma, C. Li, Z. Wen, J. Zheng, Z. Zhou, High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network, Sci. Rep. 13 (1) (2023) 2124.
- G.M. Ludovici, M.G. Cascone, T. Huber, A. Chierici, P. Gaudio, S.O. de Souza, F. d'Errico, A. Malizia, Cytogenetic bio-dosimetry techniques in the detection of dicentric chromosomes induced by ionizing radiation: a review, The European Physical Journal Plus 136 (5) (2021) 482.
- T. Lorch, J. Bille, M. Frieben, G. Stephan, An Automated Biological Dosimetry System, Architectures and Algorithms for Digital Image Processing III, SPIE, 1986, pp. 199-206.
- J. Piper, J. Sprey, Adaptive classifiers for dicentric chromosomes, J. Radiat. Res. 33 (Suppl_1) (1992) 159-170.
- H. Romm, E. Ainsbury, S. Barnard, L. Barrios, J.F. Barquinero, C. Beinke, M. Deperas, E. Gregoire, A. Koivistoinen, C. Lindholm, J. Moquet, U. Oestreicher, R. Puig, K. Rothkamm, S. Sommer, H. Thierens, V. Vandersickel, A. Vral, A. Wojcik, Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res. 756 (1-2) (2013) 174-183.
- A.A. S, J. Samarabandu, J. Knoll, W. Khan, P. Rogan, An accurate image processing algorithm for detecting FISH Probe Locations relative to chromosome Landmarks on DAPI stained metaphase chromosome images, in: 2010 Canadian Conference on Computer and Robot Vision, 2010, pp. 223-230.
- A.S. Arachchige, J. Samarabandu, J.H. Knoll, P.K. Rogan, Intensity integrated Laplacian-based thickness measurement for detecting human metaphase chromosome centromere location, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. 60 (7) (2013) 2005-2013.
- Y. Li, J.H. Knoll, R.C. Wilkins, F.N. Flegal, P.K. Rogan, Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing, Microsc. Res. Tech. 79 (5) (2016) 393-402.
- A.S. Arachchige, J. Samarabandu, J. Knoll, W. Khan, P. Rogan, An image processing algorithm for accurate extraction of the centerline from human metaphase chromosomes, in: 2010 IEEE International Conference on Image Processing, IEEE, 2010, pp. 3613-3616.
- X. Bai, L.J. Latecki, W.-Y. Liu, Skeleton pruning by contour partitioning with discrete curve evolution, IEEE Trans. Pattern Anal. Mach. Intell. 29 (3) (2007) 449-462.
- A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Curran Associates Inc., 2012, pp. 1097-1105.
- K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).
- K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
- G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700-4708.
- M. Tan, Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, PMLR, 2019, pp. 6105-6114.
- M.S. Al-Kharraz, L.A. Elrefaei, M.A. Fadel, Automated system for chromosome karyotyping to Recognize the Most Common Numerical Abnormalities using deep learning, IEEE Access 8 (2020) 157727-157747.
- W. Zhang, S. Song, T. Bai, Y. Zhao, F. Ma, J. Su, L. Yu, Chromosome classification with convolutional neural network based deep learning, 2018 11th international congress on image and signal processing, in: Biomedical Engineering and Informatics (CISP-BMEI), IEEE, 2018, pp. 1-5.
- Y. Wu, Y. Yue, X. Tan, W. Wang, T. Lu, End-to-end chromosome Karyotyping with data augmentation using GAN, in: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 2018, pp. 2456-2460.
- N. Xie, X. Li, K. Li, Y. Yang, H.T. Shen, Statistical Karyotype Analysis using CNN and Geometric optimization, IEEE Access 7 (2019) 179445-179453.
- C. Lin, G. Zhao, Z. Yang, A. Yin, X. Wang, L. Guo, H. Chen, Z. Ma, L. Zhao, H. Luo, T. Wang, B. Ding, X. Pang, Q. Chen, CIR-net: automatic classification of human chromosome based on Inception-ResNet Architecture, IEEE ACM Trans. Comput. Biol. Bioinf 19 (3) (2022) 1285-1293.
- M. Sharma, L. Vig, Automatic chromosome classification using deep attention based sequence learning of chromosome bands, in: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, 2018, pp. 1-8.
- G. Swati, M. Yadav, M. Sharma, L. Vig, Siamese networks for chromosome classification, in: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 72-81.
- S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: towards real-time object detection with region proposal networks, Adv. Neural Inf. Process. Syst. 28 (2015).
- T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2117-2125.
- F. Chollet, Others, Keras, 2015.
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: a large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Ieee, 2009, pp. 248-255.
- A. Martin, A. Ashish, B. Paul, B. Eugene, C. Zhifeng, C. Craig, S.C. Greg, D. Andy, D. Jeffrey, D. Matthieu, G. Sanjay, G. Ian, H. Andrew, I. Geoffrey, I. Michael, Y. Jia, J. Rafal, K. Lukasz, K. Manjunath, L. Josh, M. Dandelion, M. Rajat, M. Sherry, M. Derek, O. Chris, S. Mike, S. Jonathon, S. Benoit, S. Ilya, T. Kunal, T. Paul, V. Vincent, V. Vijay, V. Fernanda, V. Oriol, W. Pete, W. Martin, W. Martin, Y. Yuan, Z. Xiaoqiang, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015.
- T. O'Malley, E. Bursztein, J. Long, F. Chollet, H. Jin, L. Invernizzi, Others, KerasTuner, 2019.
- G.E. Hinton, S. Roweis, Stochastic neighbor embedding, Adv. Neural Inf. Process. Syst. 15 (2002).