Update FRETENATOR.md

Fix links, update plugin info, add extra info to frontmatter

Author: JimageJ

_pages/plugins/FRETENATOR.md

@@ -1,30 +1,38 @@
 ---
 title: FRETENATOR
-description: Fiji plugins for segmentation, quantification and ratiometric analysis of FRET biosensors. Includes a live updating GUI interface, a headless mode, a batch mode, object labelling, object classifier training and application and Trackmate 7 integration.
+description: Fiji plugins for segmentation, object classification, ratiometric analysis and intensiometric analysis. Includes a live updating GUI interface, a headless mode, a batch mode, object labelling, object classifier training and application and Trackmate 7 integration. This is under active development (as of 2026).
+update-site: FRETENATOR
+source-url: https://github.com/JimageJ/FRETENATOR2
+release-version: 2.5
+dev-status: Active
+support-status: Active
 categories: [Segmentation,  Ratiometric Imaging, 3D, Analysis]
 ---
+
 # FRETENATOR2- Comprehensive segmenation and ratiometric analysis
 
-Latest version of FRETENATOR segmentation and ratiometric analysis suite. This is currently under active development (2026 05 06).
+Fiji plugins for segmentation, object classification, ratiometric analysis and intensiometric analysis. Includes a live updating GUI interface, a headless mode, a batch mode, object labelling, object classifier training and application and Trackmate 7 integration. This is under active development with latest notes on this [github repository](https://github.com/JimageJ/FRETENATOR2) (as of 2026).
 
 New FRETENATOR2.5 implemented features:
 
-* Improved user interface,
-* Background subtraction (Global mean or Local label based)
-* Pixel by pixel processing
-* Setting saving/reloading
-* Headless mode for batch
-* 2D image processing
-* WEKA based label classifier training, using graph features for context
-* WEKA based label classification
-* Re-editing and annotation through ROI labeller
-* Bug fixes
+  * Improved user interface
+  * Background subtraction (Global mean or Local label based)
+  * Pixel by pixel processing
+  * Segmentation settings saving/loading and importing from files
+  * Headless mode for batch
+  * 2D image processing
+  * WEKA based label classifier training and classification, using graph features for context
+  * Re-editing and annotation through ROI labeller
+  * Help button linking to tutorials (this github readme)
+  * Replaced default 'Max projection' with 'Average projection'
+  * 32 bit emission ratio image outputs (no 1000X multiplied 16 bit images) with background now assigned as NaN allowing manual ROI quantification
+  * Improved pixel by pixel processing - Z projections will be an average projection of the ratioed 3D image rather than Z sum projections ratioed 
+  * Quickload segmentation settings buttons
+  * Bug fixes
+  * Separate 'Segment and intensity' plugin implementation to quantify the intensity of each channel for every ROI.
 
 Planned features:
-* Video tutorials of new interface/features
-* 32 bit emission ratio image outputs (no 1000X multiplication)
-* Quick segmentation settings button
-* Separate 'Segment and quantify' plugin implementation
+  * Video tutorials of new interface/features
 
 ### **Installation**
 
@@ -38,7 +46,7 @@ Install activating update sites:
 * Scroll down to I and make sure the IJPB is selected
 * Accept the dialogs, allow installation, then restart ImageJ
 
-## **FRETENATOR2_Segment_and_ratio**
+## **_FRETENATOR2_Segment_and_ratio_**
 
 
 ![nlsABACUS2 confocal image](https://github.com/JimageJ/FRETENATOR2/blob/main/imagefiles/image20.gif)
@@ -48,64 +56,23 @@ Install activating update sites:
 
 ## **Usage**:
 
-*FN2 Segment and ratio* is a powerful plugin to quickly perform ratiometric analysis of 2D, 3D or 4D microscopy images,  with an new user interface and a live updating preview. The plugin performs full 3D segmentation of images, which means you don't analyse background, and does all the analysis, ready for you to plot and interpret. The algorithm can be used to analyse punctate sensors (e.g. nuclear localised) on a per object basis, or diffuse sensors (e.g. cytoplasmic) on a pixel by pixel basis, with guideline settings below. Saturated pixels are automatically removed. Settings can be saved and used for headless processing, or even batch (alpha).
-
-• Results Table:    ◦ Includes the ratiometric calculation (emission ratio) your channel quantifications, and x, y, z positions. This can be saved as a .csv and then analysed in python, R or excel.
-
-• Threshold map:    ◦ An image of the initial thresholding use for analysis
-
-• Label map:    ◦ An image in which every nucleus is given a value that corresponds to the “label” in the results table.
-
-• Emission ratio map:    ◦ An image in which every nucleus is given the value of it’s emission ratio X 1000
-
-• Max Z projected emission ratio map:    ◦ A maximum Z projection of the emission ratio map
-
-• Nearest point emission ratio map:    ◦ A nearest point projection of the emission ratio map, with outlines added between the nuclei NB: the scale of this image is different to the original image and other images, allowing thin outlines to be drawn.
+*FN2 Segment and ratio* is a powerful plugin to quickly perform ratiometric analysis of 2D, 3D or 4D microscopy images,  with an new user interface and a live updating preview. The plugin performs full 3D segmentation of images, which means you don't analyse background, and does all the analysis, ready for you to plot and interpret. The algorithm can be used to analyse punctate sensors (e.g. nuclear localised) on a per object basis, or diffuse sensors (e.g. cytoplasmic) on a pixel by pixel basis, with quickload settings buttons. Saturated pixels are automatically removed. Settings can be saved and used for headless processing, or even batch (alpha).
 
-• Log:     ◦ Details of the image file and exact analysis settings used to keep with your metadata. Savable as a .txt file
+  • Results Table:    ◦ Includes the ratiometric calculation (emission ratio) your channel quantifications, and x, y, z positions. This can be saved as a .csv and then analysed in python, R or excel.
+  • Label map:    ◦ An image in which every nucleus is given a value that corresponds to the “label” in the results table.
+  • Emission ratio map:    ◦ An image in which every nucleus is given the value of it’s emission ratio
+  • Average Z projected emission ratio map:    ◦ An average Z projection of the emission ratio map
+  • Nearest point emission ratio map:    ◦ A nearest point projection of the emission ratio map, with outlines added between the nuclei NB: the scale of this image is different to the original image and other images, allowing thin outlines to be drawn.
+  • Log:     ◦ Details of the image file and exact analysis settings used to keep with your metadata. Savable as a .txt file
 
 
-### **FN2 SEGMENT AND RATIO TUTORIAL** 
+### **_FN2 Segment and ratio_ tutorial** 
 
-https://www.youtube.com/watch?v=OdPR_2kKuzg
+[On youtube.](https://www.youtube.com/watch?v=OdPR_2kKuzg)
 
 ### **Setting LUTs and making a colourbar**
 
-https://www.youtube.com/watch?v=rTH1vWirORI
-
-
-
-### **Settings for localised sensors, e.g. Nuclei**
-
-
-Switch on 'Difference of Gaussian instead of Gaussian'
-
-Set the small DoG filter between 0.5-1.2, and the large DoG filter about half the diameter of a typical nucleus in pixels
-
-Autosegmentation method : Otsu
-
-Switch on 'Watershed Object splitting' if your nuclei are close together
-
-Switch OFF 'Use pixel by pixel analysis' to allow quantification to be performed on a per object basis
-
-Max intensity: 4094 for 12 bit images, 65534 for 16 bit images
-
-
-
-### **Settings for diffuse sensors (e.g. cytoplasmic)**
-
-
-Switch OFF 'Difference of Gaussian instead of Gaussian'
-
-Set the small DoG filter between 0.5-1.2 the large DoG filter isn't used
-
-Autosegmentation method : Otsu
-
-Switch OFF 'Watershed Object splitting'
-
-Switch ON 'Use pixel by pixel analysis' to allow quantification to be performed on a per pixel basis
-
-Max intensity: 4094 for 12 bit images, 65534 for 16 bit images
+[On youtube.](https://www.youtube.com/watch?v=rTH1vWirORI)
 
 
 
@@ -127,34 +94,34 @@ The "nearest point Z projection" option has outline drawing between segmented ob
 
 There are two background subtraction methods. Global mean subtraction, subtracts the average intensity of the are excluded from segmentation in each channel from each pixel before performing calculation - this is good for the global background signal that is present in many camera/detector types. Local label based subtraction will process each ROI object individually, subtracting the average intensity of nearby pixels in the excluded area surrounding it, which is good for global background as well as local background such as light scattering/autofluorescence.
 
-## **FN2_SaR_Headless**
+## **_FN2_SaR_Headless_**
 
 Uses the last saved settings of FRETENATOR2_Segment_and_ratio, and performs analysis without opening a dialog box (faster).
 
 
-## **FN_SaR_Batch (alpha)**
+## **_FN_SaR_Batch_ (alpha)**
 
 *Currently only reliable when run from the script editor* Uses the last saved settings of FRETENATOR2_Segment_and_ratio, and performs analysis on all images in a user defined folder, then exports the analysis into another user defined folder.
 
 
-## **FN_ROI_Labeller (Beta)**
+## **_FN_ROI_Labeller_**
 
 ### Implementation and usage
 
 A follow on tool for after segmentations where users can categorise the ROI in their segmented images. As a work in progress, it currently works on single timepoint 3D label images, allowing users to visually assign labels to one of 10 categories. Results are either output to an existing results table or can be used to measure a chosen image. ***Alpha functionality:*** In the latest version, time course analysis can be performed, but usage asumes the same label usage through time (making it compatable with Trackmate exported files - see below).
 
 **Usage:**
 FRETENATOR ROI Labeller tutorial
-https://www.youtube.com/watch?v=EKXR4z5g8Pg
+[On youtube.](https://www.youtube.com/watch?v=EKXR4z5g8Pg)
 
-## **FN_Trackmate_Bridge (Alpha)**
+## **_FN_Trackmate_Bridge_ (Alpha)**
 
 A simple plugin to allow **Trackmate 7** analysed label images (Analyse the FRETENATOR label map for tracking then export the tracked label map as dots) to be combined with **FRETENATOR_Segment_and_ratio** output. This adds TrackIDs to the results table and creats a new TrackID labelmap that can be analysed with the ROI manager.
 
 ![Stomata](https://github.com/JimageJ/FRETENATOR2/blob/main/imagefiles/image29.gif)
 ![Stomata ROI labeled image after tracking with Trackmate](https://github.com/JimageJ/ImageJ-Tools/blob/master/images/labeled%20stomata.gif)
 
-## **FRETENATOR2_Segment_and_ratio_BT (Alpha)**
+## **_FRETENATOR2_Segment_and_ratio_BT_ (Alpha)**
 
 A specialised version of FRETENATOR2_Segment_and_ratio, developed for Tang et al 2025, which measures the fluorescence of an additional channel, in a dilated area surrounding and including the original ROI. This allows nearby fluorescence to be quantified and is included on the results table. Please select 'Local Label Based' from the "Background Subtraction Method" to use this functionality. This will be added as a new column to the results table.