In contrast, using the combination of Register Virtual Slices and Transform Virtual Slices required the following steps: The advantage of something like HyperStackReg is that it takes the hyperstack and outputs a hyperstack. None of them are hard, and they could all be automated with a macro, but for a beginner user it makes it much harder. The problem I found with the approach is that it is not very user friendly if you are starting with the data in hyperstack form, as it requires a lot of extra data handling and organization steps. Thanks a lot for pointing this out! I tried your suggested workflow and it was able to register effectively and then use the transform in one channel and apply it to another. Maybe some component of the Linear Stack with Shift plugin could be reused. I don’t really know how to offset the images based off the XY coordinates, so someone else will need to figure that out. When I use parseFloat to convert the string to float values, the coordinate offsets get rounded to the thousandth place, although I don’t think this is an issues for offsetting the images. ![]() YPos = YPosTemp.substring(0,YPosTemp.length-2) XPos = XPosTemp.substring(0,XPosTemp.length-1) The XY positions are saved to two arrays, xShift and yShift. You could try parsing the XY offsets from the Log file generated after running the “Linear Stack with Shift” plugin and then applying these offsets to the different channels.ĭear is an example macro function which can parse the XY coordinates. From what I can determine, the last number of the first array (-5.176) corresponds to the X offset between 2 timepoints and the last number of the second array corresponds to the Y offset (-1.102) between the 2 timepoints. Looking at the Transformation Matrix, 2 arrays are listed with 3 numbers in each array. Transformation Matrix: AffineTransform, ] ĥ5 potentially corresponding features identified Here is an example output: Processing SIFT. This Matrix describes the XY offset between neighboring frames which could then be applied to different channels. If anyone has any ideas, they would be most welcome.ĭear looked into the "“Linear Stack with Shift” plugin and one of the options when running to plugin is to output the Transformation Matrix. Hence, I would like to use my highest quality channel to do the registration and then apply the same transformations to the other, lower quality channels. This is due to a fluorescent protein expressed at low levels, sparsely and only occasionally in the cell cycle. I also cannot use the Linear Stack Alignment with SIFT on each channel independently and then merge them because one of the channels it too noisy for the registration to work properly. I have tried using other registration algorithms that have the ability to port one channel’s transformation to another channel (like MultiStackReg, or descriptor-based series registration), but they perform poorly with my dataset. Does anyone know of a way I can do this? I could not find any threads in image.sc discussing this scenario. ![]() ĬellProfiler Fiji Icy Python cell imaging image analysis object tracking time lapse.I have a hyperstack with 3 channels and over 200 timepoints and would like to register one of the channels with the “Linear Stack Alignment with SIFT” algorithm and then apply the transformations calculated in that channel to the two other ones. ![]() Traxtile is implemented in Python version 2.7 using standard distribution libraries (available at and is freely available at. Reports summarize events from the validated tracks. Links between cells in successive frames can be reviewed and edited, yielding validated tracks for the image series. For each such event, the object track is displayed on a montage of images centered on the event and spanning the preceding and subsequent frames. Traxtile imports initial assignments and automatically identifies events needing review (i.e., apparent creation of new objects, splits, merges, and losses). Here we describe Traxtile, a program that allows interactive graphical review and revision of object tracking assignments. This is challenging, as CellProfiler produces only tabular data for object tracking, and the graphical tools in Icy and Fiji are not optimal for manual review of these events. However, object tracking algorithms are imperfect, and validation of significant events is often required. Open source software packages such as CellProfiler, Icy, and Fiji provide robust and convenient interfaces for performing such analyses. Time-lapse imaging can be used to quantify how cells move, divide, and die over time and under defined culture conditions.
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