Diffeomorphic multi-frame non-rigid registration of cell nuclei in 2D and 3D live cell images
To gain a better understanding of cellular and molecular processes, it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. Since, generally, the subcellular particles move and cell nuclei move as well as deform, it is important to decou...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
16 January 2017
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| In: |
IEEE transactions on image processing
Year: 2017, Jahrgang: 26, Heft: 3, Pages: 1405-1417 |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2017.2653360 |
| Online-Zugang: | Resolving-System, Volltext: https://doi.org/10.1109/TIP.2017.2653360
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| Verfasserangaben: | Marco Tektonidis and Karl Rohr |
MARC
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| 245 | 1 | 0 | |a Diffeomorphic multi-frame non-rigid registration of cell nuclei in 2D and 3D live cell images |c Marco Tektonidis and Karl Rohr |
| 264 | 1 | |c 16 January 2017 | |
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| 520 | |a To gain a better understanding of cellular and molecular processes, it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. Since, generally, the subcellular particles move and cell nuclei move as well as deform, it is important to decouple the movement of particles from that of the cell nuclei using non-rigid registration methods. We have developed a diffeomorphic multi-frame approach for non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images. Our non-rigid registration approach is based on local optic flow estimation, exploits information from multiple consecutive image frames, and determines diffeomorphic transformations in the log-domain, which allows efficient computation of the inverse transformations. To register single images of an image sequence to a reference image, we use a temporally weighted mean image, which is constructed based on inverse transformations and multiple consecutive frames. Using multiple consecutive frames improves the registration accuracy compared to pairwise registration, and using a temporally weighted mean image significantly reduces the computation time compared with previous work. In addition, we use a flow boundary preserving method for regularization of computed deformation vector fields, which prevents from over-smoothing compared to standard Gaussian filtering. Our approach has been successfully applied to 2D and 3D synthetic as well as real live cell microscopy image sequences, and an experimental comparison with non-rigid pairwise, multi-frame, and temporal groupwise registration has been carried out. | ||
| 650 | 4 | |a fluorescence | |
| 650 | 4 | |a Microscopy | |
| 650 | 4 | |a optical microscopy | |
| 650 | 4 | |a Three-dimensional displays | |
| 650 | 4 | |a 2D live cell fluorescence microscopy images | |
| 650 | 4 | |a 3D live cell fluorescence microscopy images | |
| 650 | 4 | |a Biomedical image processing | |
| 650 | 4 | |a biomedical optical imaging | |
| 650 | 4 | |a cell nuclei | |
| 650 | 4 | |a cellular biophysics | |
| 650 | 4 | |a cellular processes | |
| 650 | 4 | |a computed deformation vector fields | |
| 650 | 4 | |a diffeomorphic multiframe nonrigid registration | |
| 650 | 4 | |a diffeomorphic registration | |
| 650 | 4 | |a diffeomorphic transformations | |
| 650 | 4 | |a flow boundary preserving method | |
| 650 | 4 | |a image registration | |
| 650 | 4 | |a image sequence analysis | |
| 650 | 4 | |a image sequences | |
| 650 | 4 | |a Image sequences | |
| 650 | 4 | |a inverse transformations | |
| 650 | 4 | |a inverse transforms | |
| 650 | 4 | |a local optic flow estimation | |
| 650 | 4 | |a log-domain | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a microscopy | |
| 650 | 4 | |a molecular processes | |
| 650 | 4 | |a multiple consecutive frames | |
| 650 | 4 | |a multiple consecutive image frames | |
| 650 | 4 | |a nonrigid pairwise registration | |
| 650 | 4 | |a Optical imaging | |
| 650 | 4 | |a Optical microscopy | |
| 650 | 4 | |a over-smoothing | |
| 650 | 4 | |a particle movement | |
| 650 | 4 | |a real live cell microscopy image sequences | |
| 650 | 4 | |a reference image | |
| 650 | 4 | |a registration accuracy | |
| 650 | 4 | |a Shape | |
| 650 | 4 | |a standard Gaussian filtering | |
| 650 | 4 | |a subcellular particle motion | |
| 650 | 4 | |a temporal groupwise registration | |
| 650 | 4 | |a temporally weighted mean image | |
| 650 | 4 | |a Two dimensional displays | |
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| 773 | 1 | 8 | |g volume:26 |g year:2017 |g number:3 |g pages:1405-1417 |g extent:13 |a Diffeomorphic multi-frame non-rigid registration of cell nuclei in 2D and 3D live cell images |
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