![]() The merged image was generated by ImageJ > Image > Color > Merge Channels… (d) Embryos at 5.5 days after fertilization (E5.5). In other words, four (2 × 2) combinations of merged images can be generated. ![]() (c) A merged image is shown where images of nuclei can be the first or the rotated second image, and particle images constructed in Figure 4 can be the first or the rotated second image. Yellow, examples of paired nuclei between the first and the rotated second image. (b) Two z‐slices of the merged image of blastocyst #1 are shown. Note that before the rotation, the intensities of the second images were normalized along the z‐axis (ImageJ > Process > Enhance Contrast… > Normalize), and thus, the intensities were not conserved. The second images before rotation are shown in Figure 2 for #1 or in the bottom panel for #2. (a) 3D images of the first and the rotated second image are shown for two blastocysts (#1 and #2). Three‐dimensional (3D) reconstruction of a rotated image. The original images were 8‐bit images where the intensities of each particle correspond to the IDs of the objects, and the color was provided by setting lookup tables (ImageJ > Image > Lookup Tables > 3‐3‐2‐RGB) Arrows, three examples of paired objects. (d) Paired objects between the first and second image are depicted as particles in the same color. Four objects in the first image are shown. For each object of interest in the first image, three objects as candidates for pairing are shown in the second image according to the distances between the objects. Yellow circles, some examples of paired objects light blue circles with dashed lines, a few examples of unsuccessfully paired objects. (b) Objects of interest in the first and second images are depicted as particles in 3D images. The 3D images were generated by using Fiji > Plugins > 3D Viewer all 3D images in this article were generated by the 3D Viewer. Images before and after the rotation of the second image are shown. (a) Landmarks in the first and second images are depicted as particles in three‐dimensional (3D) images. Registration of landmarks and objects of interest. In the case of 3D rotation, the rotation matrix contains three angles ( ϕ, θ, and ψ in Appendix A) In the case of two‐dimensional (2D) rotation, the rotation matrix contains one angle ( θ in Appendix A). At step 6, the second image is rotated to be aligned with the first image, and the rotated image is reconstructed. At step 5, the paired objects are identified (e.g., 5–12, 6–11). At step 4, the landmarks in the second image are optimally rotated. If shrinkage or elongation of the images has already been corrected at the “Optional” step, step 3 is not required. At step 3, shrinkage or elongation of the xyz‐coordinates of the landmarks and the objects of interest is corrected. At step 2, objects of interest are labeled by non‐overlapped numbers between the two images (#5–8 vs. At step 1, four landmarks are shown (#1–4). At the “Optional” step, the shrinkage or elongation of the two images is corrected (arrows). At step 0, microscopic images are shown where the second image is rotated compared with the first image. (a) The procedures of our method are illustrated. Procedures of three‐dimensional (3D) registration and reconstruction. Development, Growth & Differentiation published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Developmental Biologists. ImageJ image registration mouse early embryo three-dimensional image rotation. This approach provides a versatile tool applicable for various tissues and species. We demonstrated that this tool successfully achieved 3D registration and reconstruction of images in mouse pre- and post-implantation embryos, where one image was obtained during live imaging and another image was obtained from fixed embryos after live imaging. Furthermore, 3D rotation is applied to one of the two images, resulting in reconstruction of 3D rotated images. By simultaneously providing multiple points (e.g., all nuclei in the regions of interest) other than the landmarks in the two images, the correspondence of each point between the two images, i.e., to which nucleus in one image a certain nucleus in another image corresponds, is quantitatively explored. In this tool, several landmarks are manually provided in two images to be aligned, and 3D rotation is computed so that the distances between the paired landmarks from the two images are minimized. Here we developed an ImageJ-based tool which allows for 3D registration accompanied with both quantitative evaluation of the accuracy and reconstruction of 3D rotated images. However, there is no 3D registration tool easily accessible for experimental biologists. Three-dimensional (3D) registration (i.e., alignment) between two microscopic images is very helpful to study tissues that do not adhere to substrates, such as mouse embryos and organoids, which are often 3D rotated during imaging.
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