Automated comet assay segmentation using combined dot enhancement filters and extended-maxima transform watershed segmentation

Yıl 2023, Cilt: 4 Sayı: 2, 92 - 98, 30.08.2023

Lavdie Rada

https://doi.org/10.51753/flsrt.1319546

Öz

The comet assay, also known as single-cell gel electrophoresis, is a widely used and reliable method for assessing DNA damage and repair in individual cells. It plays a crucial role in the assessment of genetic damage potential and human biomonitoring studies in the medical and biological fields. Ensemble of comet assay individual cells and establishing accurate information on the occurrence of cellular injury followed by the process of cellular restoration is a challenging task. This paper introduces an algorithm for the detection of a distinct head, composed of undamaged DNA, and a tail, comprising damaged or fragmented DNA, in genotoxicity testing images, and provides information on the region properties of such images. The proposed approach combines a dot enhancement filter to distinguish and help in the detection of the head in each cell combined with a multilevel segmentation approach consisting of a watershed-geodesic active contour model that is capable to refine the tail estimation. The effectiveness of the suggested algorithm is quantitatively evaluated with annotation data provided by biologists, and its results are compared with those obtained from previous works. The proposed system exhibits comparable or superior performance to the existing systems while avoiding excessive computational costs.

Anahtar Kelimeler

Comet assay, dot enhancement filters, extended-maxima transform, image processing, segmentation, single cell gel, watershed

Teşekkür

We would like to express our sincere gratitude to Dr. Elda Pacheco-Pantoja from Mexico Medicine School, Health Sciences Division, Universidad Anahuac Mayab,

Kaynakça

• Afiahayati A. E., Yanuaryska R. D., & Mulyana, S. (2022). GamaComet: A deep learning-based tool for the detection and classification of DNA damage from buccal mucosa comet assay images. Diagnostics (Basel), 12(8), 2002.
• Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10, 266-277.
• Chatterjee, N., & Walker, G. C. (2017). Mechanisms of DNA damage, repair, and mutagenesis. Environmental and Molecular Mutagenesis, 58(5), 235-263.
• Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26, 297-302.
• Fairbairn, D. W., Olive, P. L., & O’Neill, K. L. (1995). The comet assay: a comprehensive review. Mutation Research/Reviews in Genetic Toxicology, 339, 37-59.
• Ganapathy, S., Muraleedharan, A., Sathidevi, P. S., Chand, P., & Rajkumar, R. P. (2016). CometQ: An automated tool for the detection and quantification of DNA damage using comet assay image analysis. Computer Methods and Programs in Biomedicine, 133, 143-154.
• Le Guyader, C., & Gout, C. (2008). Geodesic active contour under geometrical conditions: Theory and 3D applications. Numerical algorithms, 48, 105-133.
• Gyori, B. M., Venkatachalam, G., Thiagarajan, P., Hsu, D., & Clement, M. V. (2014). OpenComet: An automated tool for comet assay image analysis. Redox Biology, 2, 457-465.
• Hafiyan, Y. T., Yanuaryska, R. D., Anarossi, E., Sutanto, V. M., Triyanto, J., & Sakakibara, Y. (2021). A hybrid convolutional neural network-extreme learning machine with augmented dataset for DNA damage classification using comet assay from buccal mucosa sample. International Journal of Innovative Computing, Information and Control, 17(4), 1191-11201.
• Helmma, C., & Uhl, M. (2000). A public domain image-analysis program for the single-cell gel-electrophoresis (comet) assay. Mutagenesis, 466, 9-15.
• Lee, T., Lee, S., Sim, W. Y., Jung, Y. M., Han, S., Won, J. H., ... & Yoon, S. (2018). HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment. BioMed Central Bioinformatics, 19(1), 49-61.
• Li, Q., Sone, S., & Doi, K. (2003). Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Medical Physics, 30, 2040-2051.
• Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30, 77-116.
• Ostling, O., & Johanson, K. (1984). Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells. Biochemical and Biophysical Research Communications, 123, 291-298.
• Qin, Y., Wang, W., Liu, W., & Yuan, N. (2013). Extended-maxima transform watershed segmentation algorithm for touching corn kernels. Advances in Mechanical Engineering, 5, 268046.
• Rada, L., Erdil, E., Argunsah, A. O., Unay, D., & Cetin, M. (2014). Automatic dendritic spine detection using multiscale dot enhancement filters and sift features. 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, 26-30.
• Ruz-Suarez, D., Martin-Gonzalez, A., Brito-Loeza, C., & Pacheco-Pantoja, E. L. (2022). Convolutional neural network for segmentation of single cell gel electrophoresis assay. In: Brito-Loeza C., Martin-Gonzalez A., Castañeda-Zeman V., Safi A. (eds) International Symposium on Intelligent Computing Systems (pp. 57-68). Cham: Springer International Publishing.
• Singh, N. P., McCoy, M. T., Tice, R. R., & Schneider, E. L. (1988). A simple technique for quantitation of low levels of DNA damage in individual cells. Experimental Cell Research, 175(1), 184-191.
• Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12(5), 91.
• Uthirapathy, S. (2023). Cytostatic effects of avocado oil using single-cell gel electrophoresis (comet assay). Aro-The Scientific Journal of Koya University, 11(1), 16-21.