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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12676 |
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| _version_ | 1866918297338904576 |
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| author | Song, Yu Goh, Tatsuaki Li, Yinhao Dong, Jiahua Miyashima, Shunsuke Iwamoto, Yutaro Kondo, Yohei Nakajima, Keiji Chen, Yen-wei |
| author_facet | Song, Yu Goh, Tatsuaki Li, Yinhao Dong, Jiahua Miyashima, Shunsuke Iwamoto, Yutaro Kondo, Yohei Nakajima, Keiji Chen, Yen-wei |
| contents | Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover novel theories underlying plant growth and cell division, accurate cell tracking on live imaging is of utmost importance. The commonly used cell tracking software, TrackMate, adopts tracking-by-detection fashion, which applies Laplacian of Gaussian (LoG) for blob detection, and Linear Assignment Problem (LAP) tracker for tracking. However, they do not perform sufficiently when cells are densely arranged. To alleviate the problems mentioned above, we propose an accurate tracking method based on Genetic algorithm (GA) using knowledge of Arabidopsis root cellular patterns and spatial relationship among volumes. Our method can be described as a coarse-to-fine method, in which we first conducted relatively easy line-level tracking of cell nuclei, then performed complicated nuclear tracking based on known linear arrangement of cell files and their spatial relationship between nuclei. Our method has been evaluated on a long-time live imaging dataset of Arabidopsis root tips, and with minor manual rectification, it accurately tracks nuclei. To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm Song, Yu Goh, Tatsuaki Li, Yinhao Dong, Jiahua Miyashima, Shunsuke Iwamoto, Yutaro Kondo, Yohei Nakajima, Keiji Chen, Yen-wei Computer Vision and Pattern Recognition Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover novel theories underlying plant growth and cell division, accurate cell tracking on live imaging is of utmost importance. The commonly used cell tracking software, TrackMate, adopts tracking-by-detection fashion, which applies Laplacian of Gaussian (LoG) for blob detection, and Linear Assignment Problem (LAP) tracker for tracking. However, they do not perform sufficiently when cells are densely arranged. To alleviate the problems mentioned above, we propose an accurate tracking method based on Genetic algorithm (GA) using knowledge of Arabidopsis root cellular patterns and spatial relationship among volumes. Our method can be described as a coarse-to-fine method, in which we first conducted relatively easy line-level tracking of cell nuclei, then performed complicated nuclear tracking based on known linear arrangement of cell files and their spatial relationship between nuclei. Our method has been evaluated on a long-time live imaging dataset of Arabidopsis root tips, and with minor manual rectification, it accurately tracks nuclei. To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei. |
| title | Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.12676 |