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+# Foci-analyzer
+
+ImageJ macro for the analysis of foci (e.g. DNA damage) in nuclei (or cells). Works on 2D/3D fluorescence images, including multiseries files, as long as all series have the same dimensions.
+Timelapse images are split into separate timepoints and processed individually.
+
+Author: Bram van den Broek, The Netherlands Cancer Institute (b.vd.broek@nki.nl or bioimaging@nki.nl). @bramvdbroek on [image.sc](https://forum.image.sc/).
+
+
+
+## Workflow summary
+1. Nuclei are segmented using the pre-trained deep learning network [StarDist](https://imagej.net/plugins/stardist) (2D / 2D projection). Alternatively, the deep learning network [Cellpose](https://github.com/MouseLand/cellpose) can be used to segment nuclei or whole cells (2D, 2.5D or 3D), thanks to the [Cellpose wrapper for Fiji](https://github.com/BIOP/ijl-utilities-wrappers) by BIOP. Alternatively, classic thresholding + watershedding can be used (though no parameters can be changed).
+
+2. Foci are detected in each nucleus in 2D or 3D. After Difference of Gaussians background subtraction, local maxima are detected and used as seeds for [MorpholibJ](https://imagej.net/plugins/morpholibj)'s [marker-controlled watershed](https://imagej.net/plugins/marker-controlled-watershed) (executed on the GPU using [CLIJ2/CLIJx](https://clij.github.io/)). Additionally, AreaMaxima local maxima detection can be used as detection method.
+Thresholds are automatically calculated per image as 3 times the median of the standard deviations of the (outlier-removed) foci signal in the nuclei, and can be adapted using the threshold bias sliders.
+
+3. Foci are quantified in a single channel A, or in two channels A and B.
+For each nucleus a number of metrics are reported in a table: foci count, as well as mean/median values for foci intensity, area/volume, and whole nucleus intensity.
+If two channels are selected, foci colocalization between channels is automatically calculated.
+The table is saved as a `.tsv` file, which can be easily opened in other programs (e.g. Excel).
+
+4. Segmented nuclei and foci are visualized as overlays on the original images for easy inspection. If desired, foci detection settings (threshold bias, min/max size) can be adapted before processing all input images. A colocalization map is also created when two channels are measured.
+
+## Installation / Requirements
+
+[Activate](https://imagej.net/update-sites/following) the following Fiji update sites (in the menu bar, via Help -> Update...):
+- Foci-Analyzer
+- 3D ImageJ Suite
+- CLIJ
+- CLIJ2
+- CLIJx-assistent
+- CLIJx-assistent-extensions
+- CSBDeep
+- IJPB-plugins
+- StarDist
+- (TensorFlow) Due to a recent Fiji update, StarDist [may require the TensorFlow Update Site](https://forum.image.sc/t/stardist-error-since-update/107729) to function properly.
+
+Run Foci Analyzer from the Fiji menu: `Plugins -> Foci Analyzer -> Foci Analyzer`.
+`Combine result files` can be used to pool output `.tsv` files into a single file/table.
+
+In case you also want to use Cellpose segmentation you additionally need:
+- A working Cellpose (2.0 or higher) environment in Python (conda or venv)
+- Activated PTBIOP update site, with proper settings. See https://github.com/BIOP/ijl-utilities-wrappers/blob/master/README.md#cellpose
+
+
+## Usage manual
+
+**NOTE** This documentation is created for an older version and not fully complete. Most notably, it is missing 3D nuclei Cellpose segmentation and colocalization descriptions. Updates are coming soon!
+
+The macro starts with a large dialog containing all options and parameters (click to enlarge):
+The dialog has several sections that are discussed below. All settings in this dialog will be remembered after you click `OK`.
+
+
+
+### File handling and image settings
+
+- _Input files_ : Here you can specify which files to analyze, by adding them to the list, or drag&drop from a file explorer window
+
+- _Output folder_ : the folder where all the analyzed images and results will be written.
+
+- _Nuclei channel_ : The image channel on which nuclei segmentation will be performed. This segmentation always happens in 2D; for 3D images on a maximum intensity projection. (default: 1)
+
+- _Foci channel A_ : the first foci channel (default: 2)
+
+- _Foci channel B_ : the second foci channel (default: 3)
+
+- _Also detect foci in Channel B and perform colocalization?_ : If checked, foci in both channels *A* and *B* will be analyzed, followed by a simple colocalization analysis. (default: checked)
+
+- _3D image handling_ : This parameter determines how foci in 3D images should be analyzed. For 2D input images this setting is ignored. There are four options:
+ - *Analyze foci in 3D* (default) performs foci analysis using 3D filters and 3D marker-controlled watershed functions. Connected foci in consecutive slices are counted once.
+ - *Detect foci on a Extended Depth of Focus Projection* performs 2D foci analysis on an EDF projection.
+ - *Detect foci on the Maximum Intensity Projection* performs 2D foci analysis on the MIP projection.
+ - *Use quasi-2D foci detection (detect foci separately in every Z-slice)* analyzes every z-slice in a 3D image as a separate 2D image. This setting is useful in cases where the z-spacing is very large and each focus is visible in only one z-slice. Hence, connected foci in consecutive slices will be counted multiple times.
+ - *Process a single z-slice only (specify below which slice)* allows the user to analyze foci only in a particular z-slice.
+
+ Often analysis on 2D projections provides satisfactory results that are easier visualized/inspected compared to true 3D analysis, and much faster processing, with minimal sacrifices (e.g. foci intensities).
+
+- _[single z-slice foci detection only] Slice nr_ : the single z-slice used for the option above. For any other choice this parameter is ignored.
+
+- _Remove image borders (pixels)_ : Possibility to remove edges from the image. This can in particular be useful when the image edges have very different intensities, causing incorrect automatic nuclei segmentation. (default: 0)
+
+- _Image XY binning before analysis_ : Optional pixel binning in case the resolution is very high and the foci consist of many pixels. A value of 2 means: 2x2 pixels will be binned into 1 pixel. This reduces noise in the image and speeds up analysis. (default: 1)
+
+### Nuclei/cell segmentation settings
+
+- _Nuclei/cell segmentation method_ :
+ - *Stardist nuclei segmentation* (default) uses the pretrained convolutional neural network [StarDist](https://github.com/stardist/stardist#readme) to recognize cell nuclei in fluoresence microscopy images. In general this method works very well on a large variety of samples.
+ - *Cellpose cytoplasm segmentation* uses the deep learning network [Cellpose](https://github.com/MouseLand/cellpose#--cellpose--) (model: cyto) to recognize whole cells. Use this option if you want to measure foci in entire cells and/or you do not have a nuclear staining (but it can also work well for nuclei). Cellpose requires a few additional installations (see [Installation / Requirements](https://github.com/BioImaging-NKI/Foci-analyzer/edit/main/README.md#installation--requirements)).
+ - _Classic nuclei segmentation_ allows the user to segment nuclei using manual/automatic thresholding is provided for legacy reasons. The method is almost always outperformed by the two other methods.
+
+- _Stardist nuclei rescaling factor [1-n], 0 for automatic rescaling_ : Stardist is trained on medium resolution images, and generally performs well on images with pixel sizes around 0.5 µm. For images with much smaller pixel size StarDist tends to 'oversegment' nuclei. Set to `0` for automatic rescaling the nuclei to the optimal pixel size of 0.5 µm, or put any other number for manual control of the rescaling. (N.B. This option only affects the nuclei segmentation; it is different from the previously mentioned 'XY binning' parameter, which also changes the pixel size of the foci channels.) (default: 0)
+
+- _Probability threshold [0.0-1.0] (StarDist/Cellpose)_ : Lower values will accept more nuclei; higher values will be more stringent. For Cellpose this is actually the _flow_threshold_ parameter. (default: 0.5)
+
+- _Cellpose cell diameter (pixels), 0 for automatic_ : Estimated diameter of the cells, in pixels. Setting this parameter to 0 will trigger Cellpose to estimate it. Please check the Fiji console for the resulting estimate.
+
+- _Remove nuclei with diameter smaller than (units)_ : Objects smaller than circles having an area corresponding to this diameter will be removed. 'Units' depends on the image, and will almost always be 'µm', or otherwise 'pixels' in case the pxiel calibration values are missing. (default: 4)
+
+- _Remove nuclei with diameter larger than (units)_ : Likewise, but for large objects. (default: 50)
+
+- _Exclude nuclei on image edges_ : When checked, nuclei that touch the image edge will not be analyzed. (default: checked).
+
+- _Manual nuclei removal_ : allows the user to erase ill-segmented nuclei before analysis. (default: No thanks) Options:
+ - *No thanks* means no manual nuclei editing
+ - *Manually remove nuclei* : Remove nuclei by leftclicking them in the image with the mouse. Editings will be saved to a small text file in the output folder.
+ - *load previously saved removals (from output folder)* : If you have edited the segmented nuclei on this image before, it will load the previous nuclei removals from the file in the specified output folder. (Hence, if you change the output folder parameter this option will not work.)
+
+### Foci detection settings
+
+- _Enable foci parameters optimization mode?_ : Checking this will allow the user to adapt the foci detection settings on a preview analysis before quantifying. (default: checked)
+
+- _Foci size channel A/B (after XY binning)_ : choices between *tiny*, *small*, *average*, *large*, *huge*, and *other*. This parameter controls several foci image filtering steps and steers the macro towards detecting smaller or larger foci. (default: average).
+
+- _Foci detection method_ :
+ - *Marker-controlled watershed* uses [marker-controlled watershed](https://imagej.net/plugins/marker-controlled-watershed) with local maxima as seeds to segment foci
+ - *AreaMaximum detection* tends to detect only the peaks of the foci. Can be tried when the other option doesn't provide satisfactory results.
+
+- _Foci intensity threshold bias channel A/B_ : The macro will automatically estimate the intensity threshold for foci detection (after difference-of-Gaussians background subtraction). This default threshold is set at 3 times the median standard deviation of the nuclear background signal in all nuclei in the image. The user can bias this threshold with the slider to lower values (accepting more low intensity foci) or higher values (gearing towards high intensity foci). The bias slider couples exponentially to the used threshold value: threshold = estimated_threshold * ebias.
+Since the minimum and maximum slider values are (-2.5, 2.5) the threshold can be set to anywhere between 0.2 and 36 times the standard deviation of the nuclear background signal. (default: 0)
+
+- _Minimum foci size (pixels/voxels)_ : Foci occupying an area/volume smaller than this value (in pixel/voxels) will be deleted. Note that for the marker-controlled watershed detection method the minimum foci size is `5 pixels` for 2D images and `7 voxels` for 3D images. Hence, setting sizes smaller than this will not change the number of detected foci.
+
+- _Maximum foci size (pixels/voxels)_ : The upper limit for the foci size, in pixels/voxels.
+
+- _Max distance of foci outside nuclei/cells (units); -1 for full image_ : This controls how far (in units (=microns)) outside the cell/nucleus segmentation foci should be still be counted. (default: 0)
+
+- _Minimum overlap of foci to colocalize_ : Foci in channel A and B will be counted as colocalizing *only if they overlap with at least this area/volume (in pixels/voxels).
+
+### Visualization options
+
+- _Nuclei outline color_ : The color of the nuclei outlines overlay.
+
+- _Nuclei label color_ : The color of the nuclei label numbers overlay.
+
+- _Nuclei label size_ : The size of the nuclei label numbers overlay.
+
+- _Debug mode (show intermediate images)_ : Used for development and bug fixing: checking this option will trigger displaying many intermediate results during the processing. It will also slow down the analysis.
+
+## Tips and tricks
+When the option _Enable foci parameters optimization mode?_ is active, a preview image of the detected foci will be shown during process. A dialog appears where foci detection parameters can be adjusted:
+
+
+
+After changing the settings, clicking `OK` will detect the foci on the same nuclei with the updated parameters and redisplay them in the preview image. Channel display settings and zoom are remembered in between optimization steps.
+The preview image shows an ImageJ hyperstack that, besides the z-slices of the 3D image (if applicable), contains two *frames* representing the foci channels A and B (if applicable), and four *channels*:
+1. The nuclei staining channel (azure blue)
+2. The original foci data (green)
+3. The detected foci (magenta)
+4. The centroids of the detected foci (white)
+
+
+
+The image below shows a montage layout of such a hyperstack, with vertically the '*frames*' (foci channels A and B - here 2 and 3), and horizontally the '*channels*' (as described above):
+
+
+By changing the displayed channels (*Shift-Z*), adjusting brightness&contrast (*Shift-C*) of the channels and zooming in ('+' and '-' keys), the user can inspect the foci detections for both foci channels, adjust the detection parameters if desired and rerun the foci detection step. In particular, unchecking channel 1 (nuclei), and then alternating the displaying of channel 3 (detected foci) is most helpful.
+
+
+
+
+
+### Colocalization
+
+The macro produces overlap maps for the foci in the two chosen channels that visualize the overlap of foci in the different channels. This map has 6 channels:
+
+1. detected foci mask channel A (green)
+2. detected foci mask channel B (red)
+3. overlap % foci count with channel A (azure; the brighter, the more foci overlap)
+4. overlap % foci volume with channel A (magenta)
+5. overlap % foci count with channel B (azure)
+6. overlap % foci volume with channel B (magenta)
+
+More documentation will follow with the coming release (soon!), that includes proper (2D/3D) Cellpose segmentation, and stats for all foci instead of only averages per nucleus.