diff --git a/_pages/plugins/trackmate/detectors/trackmate-cellpose-advanced.md b/_pages/plugins/trackmate/detectors/trackmate-cellpose-advanced.md index 35472c008b..63123f2b80 100644 --- a/_pages/plugins/trackmate/detectors/trackmate-cellpose-advanced.md +++ b/_pages/plugins/trackmate/detectors/trackmate-cellpose-advanced.md @@ -8,7 +8,7 @@ artifact: sc.fiji:TrackMate-Cellpose section: TrackMate-Cellpose.:Usage:cellpose parameters in the TrackMate UI. --- -{% include img src="/media/plugins/trackmate/trackmate-cellpose-screenshot.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-screenshot.png" align='center' width='500' %} This page describes the advanced version of the cellpose detector in [TrackMate](/plugins/trackmate/index), documented [here](trackmate-cellpose). These two detectors rely both on the [cellpose](https://cellpose.readthedocs.io/en/latest/) software, but this one offers more configuration options that are hidden in the non-advanced version. @@ -33,15 +33,7 @@ In particular the _Installing cellpose_ paragraph in the _Additional informatio We describe here the advanced parameters that are specific to the advanced detector. Core parameters are described in the [TrackMate-Cellpose](trackmate-cellpose) documentation page. -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-ui-advanced-2D.png" align='center' width='300' %} - -##### `Cell probability threshold` -One of the neural network output of cellpose is a cell probability map: each pixel contains the value of its probability to belongs to a cell. -This parameter controls the amount of confidence to keep a pixel in the cell: all pixels with a probability above `cell_probability_threshold` will be kept. -This value can range from `-6.0` (more and larger cells) to `6.0` (less and smaller but most likely cells). - -_By default in the non-advanced option, this parameter is `0.0`._ - +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-ui-advanced-2D.png" align='center' width='300' %} ##### `Flow threshold` Cellpose performs an additional step after a first reconstruction of the shape to check the consistency between the calculated cell shapes and the computed flows. @@ -52,8 +44,15 @@ If it's `0`, this step will not be performed (all detections whatever the shape _By default in the non-advanced option, this parameter is `0.4`._ +##### `Cell probability threshold` +One of the neural network output of cellpose is a cell probability map: each pixel contains the value of its probability to belongs to a cell. +This parameter controls the amount of confidence to keep a pixel in the cell: all pixels with a probability above `cell_probability_threshold` will be kept. +This value can range from `-6.0` (more and larger cells) to `6.0` (less and smaller but most likely cells). + +_By default in the non-advanced option, this parameter is `0.0`._ + -##### `Resample` +##### `Do not resample` Cellpose resizes your image to have the mean cell size (`mean diameter` parameter above) equals to the mean diameter of the trained model. The flows are computed on these rescaled images. @@ -62,7 +61,11 @@ It will affect the smoothness of the resulting segmentation and the computing ti - If your mean diameter > model diameter, the image is downscaled. In this case, resampling will create smoother ROIs but will be slower (the calculation will be done in the original image size, so on larger image). - If your mean diameter < model diameter, the image is upscaled. In this case, resampling will be faster (running on smaller image), but will find less detections than without resampling. -_By default in the non-advanced option, this parameter is set to `True`._ +_By default in the non-advanced option, this parameter is set to `False`. +The images are resampled._ + + + \ No newline at end of file diff --git a/_pages/plugins/trackmate/detectors/trackmate-cellpose-sam.md b/_pages/plugins/trackmate/detectors/trackmate-cellpose-sam.md index db125df899..9d15c61f8d 100644 --- a/_pages/plugins/trackmate/detectors/trackmate-cellpose-sam.md +++ b/_pages/plugins/trackmate/detectors/trackmate-cellpose-sam.md @@ -2,12 +2,12 @@ title: TrackMate-Cellpose-SAM categories: [Segmentation,Tracking,Deep Learning] icon: /media/icons/cellpose.png -description: cellpose-SAM integration in TrackMate. +description: Cellpose-SAM integration in TrackMate. categories: [Segmentation,Tracking,Machine Learning] artifact: sc.fiji:TrackMate-Cellpose --- -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-R2_multiC.gif" width='400' %} {% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-01.png" width='200' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-R2_multiC.gif" width='400' %} {% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-01.png" width='200' %} This page describes a detector module for [TrackMate](/plugins/trackmate/index) that relies on the latest version of [cellpose](https://cellpose.readthedocs.io/en/latest/) to segment cells in 2D. It is not included in the core of TrackMate and must be installed via its own [update site](/update-sites/following). It also requires cellpose to be installed on your system and working independently. This tutorial page gives installation details and advices at how to use the cellpose integration in TrackMate. @@ -76,13 +76,13 @@ If you have not done it yet, you need to [configure the TrackMate conda path in ### Cellpose parameters in the TrackMate UI -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-01.png" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-01.png" align='center' width='400' %} We document these parameters from top to bottom in the GUI. ##### `Conda environment` Specify in what conda environment you installed Cellpose-SAM. -If you get an error at this stage, it is likely because the conda path for TrackMate was not configured properly. Check [this page]((/plugins/trackmate/trackmate-conda-path). +If you get an error at this stage, it is likely because the conda path for TrackMate was not configured properly. Check [this page](/plugins/trackmate/trackmate-conda-path). ##### `Pretrained model` @@ -121,14 +121,14 @@ This image comes from the lab of Guillaume Jacquemet and was used in {% include With all channels: -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-allchannels.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-allchannels.png" align='center' %} Channel 1 only, in which nuclei are painted in red: -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-channel-1.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-channel-1.png" align='center' %} Channel 2 only, in which the cell cytoplasm appears in green: -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-channel-2.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-channel-2.png" align='center' %} Channel 3 only, which is empty: -{% include img src="/media/plugins/trackmate/detectors/trackmate-cellpose-sam-channel-3.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-sam-channel-3.png" align='center' %} diff --git a/_pages/plugins/trackmate/detectors/trackmate-cellpose.md b/_pages/plugins/trackmate/detectors/trackmate-cellpose.md index f07659ec56..02b96d4166 100644 --- a/_pages/plugins/trackmate/detectors/trackmate-cellpose.md +++ b/_pages/plugins/trackmate/detectors/trackmate-cellpose.md @@ -8,13 +8,18 @@ artifact: sc.fiji:TrackMate-Cellpose section: TrackMate-Cellpose.:Usage:cellpose parameters in the TrackMate UI. --- -{% include img src="/media/plugins/trackmate/trackmate-cellpose-screenshot.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-screenshot.png" align='center' width='500' %} This page describes a detector module for [TrackMate](/plugins/trackmate/index) that relies on [cellpose](https://cellpose.readthedocs.io/en/latest/) to segment cells in 2D. It is not included in the core of TrackMate and must be installed via its own [update site](/update-sites/following). It also requires cellpose to be installed on your system and working independently. This tutorial page gives installation details and advices at how to use the cellpose integration in TrackMate. -cellpose is a segmentation algorithm based on Deep Learning techniques, written in Python 3 by Carsen Stringer and Marius Pachitariu. The TrackMate-cellpose module, which is written in Java, is an example of integration via sub-processes. The integration technique is similar to that of the [TrackMate-Ilastik](trackmate-ilastik) module, except that the ilastik authors offer a ready-to-use Java bridge that took care of launching ilastik from Fiji. For the TrackMate-cellpose module, we rely on launching the cellpose command-line interface directly from TrackMate. +There are other TrackMate detectors shipped in the same update site, and documented separately: +- This page documents using cellpose 3.1.1 with TrackMate. +- There is also an [advanced version](trackmate-cellpose-advanced) of this detector, that let you configure additionnal parameters. +- The latest version of cellpose (as December 2025) is cellpose-SAM (cellpose 4), for which we made a specialized detector, documented [here](trackmate-cellpose-sam). +- Omnipose is another segmentation algorithm specialized for rod-shape bacteria. Because it is a based on Cellpose, its TrackMate integration is shipped in the same upate site and documented [here](trackmate-omnipose), and here for its [advanced version](trackmate-omnipose-advanced). -If you use the cellpose TrackMate module for your research, please also cite the cellpose 3 paper: +Cellpose is a segmentation algorithm based on Deep Learning techniques, written in Python 3 by Carsen Stringer and Marius Pachitariu. +If you use the cellpose TrackMate module for your research, please also cite the Cellpose 3 paper: _{% include citation doi='10.1038/s41592-025-02595-5' %}_ @@ -27,69 +32,59 @@ We need to subscribe to an extra update site in Fiji, and have a working install In Fiji, go to {% include bc path='Help|Update...' %}. Update and restart Fiji until it is up-to-date. Then go to the update menu once more, and click on the `Manage update sites` button, at the bottom-left of the updater window. A new window containing all the known update sites will appear. Click on the **TrackMate-Cellpose** check box and restart Fiji one more time. -### conda - -You need to install conda (or mamba, or any flavor of conda) on your system. We recommend [miniforge](https://github.com/conda-forge/miniforge). - - - -### cellpose +### Conda & Cellpose This step is completely independent of Fiji. If you have already a working cellpose installation, you can skip this section entirely. But we absolutely need a working cellpose. There are many ways to get cellpose installed. We give a subset of them in the [last section of this document](#installing-cellpose). +Once this is done, TrackMate needs to know where you installed conda. +You need to do this only once. +This is documented [on this page](/plugins/trackmate/trackmate-conda-path). + ## Usage ### Cellpose parameters in the TrackMate UI -{% include img src="/media/plugins/trackmate/trackmate-cellpose-ui-01.png" align='center' width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-ui-01.png" align='center' width='300' %} We document these parameters from top to bottom in the GUI. - +Specify in what conda environment you installed Cellpose (the v3.1.1 in the case of this detector). +If you get an error at this stage, it is likely because the conda path for TrackMate was not configured properly. +Again, check [this page]((/plugins/trackmate/trackmate-conda-path) if this happens. -##### `Path to cellpose / Python executable` -We must specify where is the cellpose executable, as it was installed outside of Fiji. Because there are several ways of installing cellpose (with Conda or using the precompiled executables), we have to accommodate several cases we document [later in this document](#installing-cellpose). Briefly: +##### `Pretrained model` -- If you installed cellpose via the precompiled executables, enter the _path to the executable_. For instance: `C:\Users\tinevez\Applications\cellpose.exe` -- If you installed cellpose via Conda, enter the _path to the Python executable of the Conda environment you created for cellpose_. For instance: `C:\Users\tinevez\anaconda3\envs\cellpose\python.exe` +This list lets you select the pretrained models that were shipped with Cellpose 3. +They are dully documented in details on the [Cellpose doc website](https://cellpose.readthedocs.io/en/v3.1.1.1/models.html). +The 'main' ones are: + +- the `cyto3` model, that excels at segmenting cells stained for their cytoplasm. This model supports the specification of a second channel (see below) where nuclei are stained, to guide cell sgmentation. +- the `nuclei` model, trained to segment cell nuclei. -##### `Pretrained model` -Right now we only support three pretrained models of cellpose: -- the `cyto` model ("Cytoplasm"), to segment cells stained for their cytoplasm; -- the `nuclei` model ("Nuclei") to segment cell nuclei; -- the `cyto2` model ("Cytoplasm 2.0"), which is the `cyto` model augmented with user-submitted images. -- the `live cell` model from Cellpose was trained on the `LIVECELL` [dataset](https://sartorius-research.github.io/LIVECell/) of label free images of cells. -- the `TissueNet` model from Cellpose was trained on the `TissueNet` [dataset](https://datasets.deepcell.org/data) available from deepcell. -- "Custom" lets you specify a custom model you would have trained or downloaded, which is documented below. ##### `Path to custom model` -If in the `Pretrained model` selection you pick `Custom`, a text field will appear above, allowing for entering the path to a custom cellpose model. You need to browse to the actually model file generated by the training. For instance: `D:\Projects\Brightfield\Cellpose model 171121-20211118T111402Z-001\171121\cellpose_residual_on_style_on_concatenation_off_train_folder_2021_11_17_19_45_23.398850` +There are radio buttons before the `Pretrained model` and `Path to custom model` that lets you select whether you want to use a pretrained model, or a custom one. +When `Path to custom model` is selected, you need to browse to the actual model file. Note that it must be the an `absolute path` to the file. -##### `Channel to segment` +##### `Target channel` If your image has several color channels, choose here the channel to segment. The number of the channel corresponds to the _imagej channel_ in your image. It should be the channel where the cytoplam staining is for cell segmentation, or the nuclei staining for nuclei segmentation, e.g. with the `nuclei` model. Note that TrackMate doesn't handle RGB stacks so you might need to convert your image if it is not already a composite image (see below for [tip on how to pass RGB images to TrackMate-Cellpose](#tip-passing-rgb-images-to-trackmate-for-cellpose)) -##### `Optional second channel` +##### `Second optional channel` -The `cyto` and `cyto2` pretrained models have been trained on images with a second channels in which the cell nuclei were labeled. It is used as a seed to make the detection of single cells more robust. +The `cyto3` (and `cyto2` and `cyto`) pretrained models have been trained on images with a second channels in which the cell nuclei were labeled. It is used as a seed to make the detection of single cells more robust. It is optional and this parameter specifies in which channel are the nuclei (the number of the imagej channel of the nuclei). Use '0' to skip using the second optional channel. For the `nuclei` model, this parameter is ignored. @@ -98,9 +93,19 @@ For the `nuclei` model, this parameter is ignored. cellpose can exploit an _a priori_ knowledge on the size of cells. If you have a rough estimate of the size of the cell, enter it here. In TrackMate this parameter must be specified in physical units (for instance µm if the source image has a pixel size expressed in µm). Enter the value '0' to have cellpose automatically determine the cell size estimate. +_Careful!_ I insist a bit on this point. +If you are used to running Cellpose from the Cellpose UI or a Python script, it expects the diameter to be in pixels. +But here in TrackMate, _the diameter is in the spatial units of your image_. + ##### `Use GPU` -If this box is checked, the GPU will be used _if cellpose was installed with required librairies and hardware to use the GPU_. If the GPU support is absent or incorrect, this setting will be safely ignored and the computation will rely on CPU only. Unchecking the box will force cellpose to use the CPU even if GPU support is available. +If this box is checked, the GPU will be used _if cellpose was installed with required librairies and hardware to use the GPU_. +If the GPU support is absent or incorrect, this setting will be safely ignored and the computation will rely on CPU only. +Unchecking the box will force cellpose to use the CPU even if GPU support is available. + +We took special care to implement multi-threaded processing if you use CPU. +With this setting, there will be one Cellpose process running per thread you set in the _Edit > Options > Memory and Threads..._ configuration ("Parallel Threads for stacks"). +And then each image corresponding to the time-points will be dispatched automatically to these multiple processes, running concurrently. ##### `Simplify contours` @@ -109,11 +114,11 @@ If this box is checked, the contour outlines generated by the masks returned by ### (Re)Training of a cellpose model -To optimize CellPose on your data, you can retrain one its model and use it in TrackMate-CellPose. You can do the retraining easily through [CellPose 2.0 GUI](https://www.nature.com/articles/s41592-022-01663-4). When the model gives acceptable results, select it in TrackMate interface with the `custom_model` parameter. +To optimize CellPose on your data, you can retrain one its model and use it in TrackMate-CellPose. You can do the retraining easily through [CellPose 2.0 GUI](https://www.nature.com/articles/s41592-022-01663-4). When the model gives acceptable results, select it in TrackMate interface with the `Path to custom model` parameter. + ## Tutorials @@ -124,7 +129,7 @@ One of the central advantage of cellpose is its ability to give robuset segment [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5864646.svg)](https://doi.org/10.5281/zenodo.5864646) -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-01" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-01" align='center' width='400' %} These are breast cancer cells moving collectively. We are tracking them to analyze how their migration parameters change in various conditions. @@ -133,39 +138,45 @@ The movie comes from RGB images that have already been split in 3 separated comp Open Fiji and load the image. Then launch TrackMate in {% include bc path='Plugins|Tracking|TrackMate' %}. In the second panel, select the **cellpose detector**: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-02" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-02" align='center' width='400' %} then click the `Next` button. You should see the configuration panel for cellpose. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-03" align='center' width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-ui-01.png" align='center' width='300' %} -We want to segment the touching cells and obtain their contour. So we will use the **Cytoplasm** model. The cytoplasm is imaged in the second channel (it happens to use the green LUT, but it would not matter) so we enter **2** for the `Channel to segment` parameter. The channel **1** contains the nuclei so we can specify it in the `Optional second channel` parameter. Finally, we can measure a rough estimate of the cell size using ImageJ ROI tools, which is about **40 µm**, and enter this value in the `Cell diameter` field. +On the `Conda environment` select the name of the environment Cellpose 3 is installed (**cellpose-3** in the example above). +We want to segment the touching cells and obtain their contour. So we will use the **cyto3** model, to be selected in the `Pretrained model` list. +The cytoplasm is imaged in the second channel (it happens to use the green LUT, but it would not matter) so we enter **2** for the `Target channel` parameter. +The channel **1** contains the nuclei so we can specify it in the `Second optional channel` parameter. +Finally, we can measure a rough estimate of the cell size using ImageJ ROI tools, which is about **40 µm**, and enter this value in the `Cell diameter` field. -The movie is quite big, and will take several minutes to process using only the CPU (on my Mac it takes about 20 minutes without multithreading). Having a cellpose installation with GPU support really accelerate segmentation, but this is not available on Mac. Instead, for we developed TrackMate-cellpose so that on Mac, it can accelerate processing using multithreading. CPU multithreading is used only on Mac and if the `Use GPU` box is unchecked, which is why it is the case on the image above. +The movie is quite big, and will take several minutes to process using only the CPU (on my Mac it takes about 20 minutes without multithreading). +Having a cellpose installation with GPU support really accelerate segmentation. Finally, for this movie we want the cell contour to follow the pixels of the masks generated by cellpose, so we unchecked the `Simplify contour` box. -Then we are ready to launch segmentation. Click the `Next` button. The log window will echo the messages from cellpose. On a windows machine with GPU support, the detection step takes about 2 minutes. On a Mac with multithreading using 8 cores, it takes about 4 minutes. +Then we are ready to launch segmentation. Click the `Next` button. The log window will echo the messages from cellpose. On a windows machine with GPU support, the detection step takes about 2 minutes. +On a 2022 MacBook Pro using GPU, it takes about 4 minutes. The **Initial thresholding** reports the quality histogram for all detections. Here, the quality is just the area of detections in pixel units. We see that there are some detections with very little size, probably for spurious objects. We can filter them out by setting a threshold value around 220. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-04" align='center' width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-04" align='center' width='300' %} Click the `Next` button. In the **Spot filter** panel TrackMate first computes all the spot features. Then the results are shown. Given the difficulty of the segmentation task, the results are surprisingly good, and we should thank cellpose for it. They are not perfect though. For some big and faint cells, we can see a few events of oversegmentation, probably caused by red granules drifting in some cells. Still, we can follow cell division by eye and see that the vast majority of them is detected properly. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-05" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-05" align='center' width='400' %} We don't need to filter the detections so click the `Next` button. Our aim is to detect cell divisions as they migrate, so we have to pick either the LAP tracker of the Overlap tracker. Given the cell density we would normally use the LAP tracker. However it would introduce too many false positive in the cell division detection. Indeed, the cells migrate "downwards" in the image, and new cells appear in the field-of-view from the top of the image. These new cells would be elected as candidates to be the daughter emerging from a non-existent division of the cell just below it. The overlap tracker will be more robust against this artifact so we elect to chose it. We use the following parameter: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-06" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-06" align='center' width='400' %} We dilate the cells with a `Scale factor` of *1.2* so that if the daughter cells moved away from the mother cell they are still linked to the mother. A `Minimal IoU` value of *0.3* mitigate false cell division detections. Choosing the *Precise* method for `IoU calculation` has some cost on tracking time (5 s. versus 1 s. for the *Fast* method) but it is so short in our case that we still use it. Here is what the tracking results look like: -{% include video src="/media/plugins/trackmate/trackmate-cellpose-tutorial-07.mp4" width='600' align="center" %} +{% include video src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-07.mp4" width='600' align="center" %} In the movie above the cells are colored by their track ID, which tends to highlight tracking mistakes and missed detections. Indeed, some cell divisions are missed. Still the results are better than what they would be with the LAP tracker, due to the bias for low Y value discussed above. @@ -173,9 +184,13 @@ In the movie above the cells are colored by their track ID, which tends to highl Another key advantage of cellpose is that it is relatively easy and fast to train or augment a model to harness difficult use cases that are not handled well by the pretrained model. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-01" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-01" align='center' width='400' %} -For instance the cells above were imaged in bright-field at high resolution. They are Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel. They have a rather complex shape and a low contrast. The segmentation of cells in such images is normally difficult with classical image processing techniques. In the following we will use the trackMate-Cellpose intragration to segment and track them. Our goal is to see if we discriminate between two types of cell locomotion based on dynamics and morphological features of single cells. We will see how to give more robustness to the analysis by filtering out some detections close to image border. +For instance the cells above were imaged in bright-field at high resolution. +They are Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel. +They have a rather complex shape and a low contrast. +The segmentation of cells in such images is normally difficult with classical image processing techniques. In the following we will use the TrackMate-Cellpose intragration to segment and track them. +Our goal is to see if we discriminate between two types of cell locomotion based on dynamics and morphological features of single cells. We will see how to give more robustness to the analysis by filtering out some detections close to image border. The data can be obtained from Zenodo: @@ -184,38 +199,37 @@ The data can be obtained from Zenodo: The dataset contains a custom cellpose model. We have trained a it using the [ZeroCostDL4Mic platform](https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki). This cellpose model was trained for 500 epochs on 214 paired image patches (image dimensions: 520x 696), with a batch size of 8, using the [Cellpose ZeroCostDL4Mic notebook (v 1.13)](https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb). The cellpose `cyto2` model was used as a training starting point. The training was accelerated using a Tesla K80 GPU. To use the model you need to unzip the `Cellpose model 171121-20211118T111402Z-001.zip` file. The cellpose model file itself is the `cellpose_residual_on_style_on_concatenation_off_train_folder_2021_11_17_19_45_23.398850` file in the `171121` folder. -We can use it in TrackMate by specifying "Custom" as a model in the interface: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-02" align='center' width='350' %} +We can use it in TrackMate by checking the radio button in front of the `Path to a custom model` parameter: +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-02" align='center' width='350' %} -Here I am running TrackMate on a Windows machine, and I used the cellpose installation recommended by BIOP people ([see below](#biop-conda-installation-for-gpu-support-on-windows)). So in the `Path to cellpose / Python executable` I have: `C:\Users\tinevez\anaconda3\envs\cellpose_biop_gpu\python.exe` - -As `Pretrained model` I picked **Custom** and in the `Path to custom model` text field I entered the path to the model file (in this case, ending in `.398850`). +For the `Path to a custom model` parameter, I browsed to the model file (in this case, ending in `.398850`). There is only one channel, so I used **0** for both the target and optional channels. The cell size measured with ImageJ is about 60 µm. -On this Windows machine and with this cellpose installation I have GPU support, so I left the corresponding box checked. Later we will measure shape descriptors for the cell, so it is important to simplify their contours, and the corresponding box is also checked. +On a 2022 MacBook Pro used in this demo, the Cellpose installation have GPU support, so I left the corresponding box checked. +Later we will measure shape descriptors for the cell, so it is important to simplify their contours, and the corresponding box is also checked. -Clicking `Next` starts the segmentation. In my case it completes in about 1 minute. In the **Initial thresholding** panel, choose a threshold of 0 to include all detections in the next step. We get the following results displayed in the **Spot filter** panel: +Clicking `Next` starts the segmentation. In my case it completes in about 30 seconds. In the **Initial thresholding** panel, choose a threshold of 0 to include all detections in the next step. We get the following results displayed in the **Spot filter** panel: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-03" width='300' %} -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-04" width='300' %} -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-05" width='150' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-03" width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-04" width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-05" width='150' %} The contours are not absolutely perfect. In a few cases they miss a cell extrusion or include a debris that touches the cell. Some cells that touch the border of the image are not detected. But overall the results are very good and there is no spurious detection. We don't need to add spot filters at tis step. For this movie, the tracking part should be simple as the cells are well separated and move by a distance smaller than their size from one frame to the next. Yet, we have to bridge over the missed detections when cells were close to the border. The **Simple LAP tracker** is suitable for this case: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-06" width='300' align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-06" width='300' align='center' %} The max distance are taken from the cell size. Note the large max frame gap, chosen to accommodate several missed detections near the image borders. As final results we get: -{% include video src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-07.mp4" width='600' align="center" %} +{% include video src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-07.mp4" width='600' align="center" %} We can distinguish roughly two modes of motion for the cells in this movie. The two cells in the bottom-left part of the image display saltatory movements with extrusion forming on changing sides of the cell, followed by a retraction and a large but brisk movement of the nucleus. The other cells display a large lamellipodium and exhibit a smooth motion in the direction of the protrusion. We can use the **Plot features** panel to investigate these behaviors quantitatively. For instance, one of the two cells in the bottom-left (in blue in the movie above and in the figure below) display this dynamics for the cell area and speed: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-08" width='600' align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-08" width='600' align='center' %} We see that brisk changes in area, corresponding to retraction events, correlate with peaks in velocity. @@ -225,57 +239,61 @@ The velocity is defined for links (edge between two detection across time), so c The same metrics are different for the cell on the right: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-09" width='600' align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-09" width='600' align='center' %} There seems to be a peak in velocity at late times along with a decrease in area, but this might be due to the cell crossing the right border of the image. We can prune these incorrect data points by filtering out the cells that touch the borders of the image. To do this, navigate back in TrackMate to the **Spot filter** panel. We will add a filter on the cell position to hide the detections too close to the border. Let's add a filter that keeps cells which center is below 420 µm: -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-10" width='300' align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-10" width='300' align='center' %} Then proceed as before for the tracking step and the plot generation. The area and velocity plots for the filtered cell track does not show the peak in velocity we had without filtering. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-tutorial-b-11" width='600' align='center' %} - +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-tutorial-b-11" width='600' align='center' %} + ## Additional informations -### Tip: Passing RGB images to TrackMate for cellpose +### Tip: Passing RGB images to TrackMate for Cellpose -On the cellpose webiste you can find a collection of test images to test with cellpose. They will work with the TrackMate integration as well, but they are RGB images. TrackMate does not support RGB images. So we give here a short optional procedure on how to feed RGB images to TrackMate and have them still segmented by cellpose as expected. Again, if you don't have RGB images as input, you can skip this section. +On the Cellpose website you can find a collection of test images to test with cellpose. They will work with the TrackMate integration as well, but they are RGB images. TrackMate does not support RGB images. So we give here a short optional procedure on how to feed RGB images to TrackMate and have them still segmented by cellpose as expected. Again, if you don't have RGB images as input, you can skip this section. cellpose can and does work with RGB images. They are single-channel but encode red, green and blue components of each pixel in one value, effectively behaving as a 3-channels 8-bit image. However, TrackMate cannot deal with RGB images. If you launch TrackMate with an RGB image you will receive an error message. The workaround is to convert them to a proper 3-channels 8-bit image before running TrackMate. cellpose will be able to run with them anyway. Here is how to do it. - With the RGB image opened, go to the {% include bc path='Image|Type' %} menu and select `RGB Stack`. This will convert the 1-channel RGB image in a 3-channels 8-bit image. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-convert-rgb-01.png" width='400' %} -{% include img src="/media/plugins/trackmate/trackmate-cellpose-convert-rgb-02.png" width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-convert-rgb-01.png" width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-convert-rgb-02.png" width='400' %} - Most likely Fiji will display only one of the 3 channels in grayscale. To overlay again the 3 channels in a composite image, first display the **Channels tool** by selecting the {% include bc path='Image|Color|Channels Tool…' %} menu item. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-convert-rgb-03.png" align='center' width='400' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-convert-rgb-03.png" align='center' width='400' %} - A new floating window title **Channels** appears. Click on the `More »` button to bring an additional menu, and select the `Make Composite` item. You image should be now properly displayed, but made of 3 independent components. This way you can run it through TrackMate and still have it properly segmented by its cellpose integration. -{% include img src="/media/plugins/trackmate/trackmate-cellpose-convert-rgb-04.png" %} -{% include img src="/media/plugins/trackmate/trackmate-cellpose-convert-rgb-05.png" %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-convert-rgb-04.png" %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-convert-rgb-05.png" %} -### Installing cellpose +### Installing Cellpose {% include notice icon="tech" content="This is the recommended way to install Python tools to be used with TrackMate." %} {% include notice icon="tech" - content="From now on we support **cellpose 3** in TrackMate. It is great, simple to install, and has GPU acceleration on all modern platforms." %} + content="This detector supports **Cellpose 3** in TrackMate. It is great, simple to install, and has GPU acceleration on all modern platforms. There is a separate detector for **Cellpose SAM**, linked above." %} -Go to the cellpose GitHub webpage and follow the [installation procedures](https://github.com/MouseLand/cellpose#local-installation). We copy and adapt these installation instructions below. +You need to have conda (or mamba) installed on your system. +Any flavor (Anaconda, miniconda, miniforge, mamba, micromamba, ... ) works, but if you have to install one, we recommend [miniforge](https://github.com/conda-forge/miniforge). -```zsh +Once it is done, go to the cellpose GitHub webpage and follow the [installation procedures](https://github.com/MouseLand/cellpose#local-installation). We copy and adapt these installation instructions below. +In a terminal where conda is usable, type the following (assuming you run zsh): + +```sh conda create --name cellpose-3 python=3.10 conda activate cellpose-3 pip install 'cellpose[gui]==3.1.1.2' @@ -321,4 +339,4 @@ torch version: 2.8.0+cu126 _____ -*Jean-Yves Tinevez - August 2025* +*Jean-Yves Tinevez - December 2025* diff --git a/_pages/plugins/trackmate/detectors/trackmate-omnipose-advanced.md b/_pages/plugins/trackmate/detectors/trackmate-omnipose-advanced.md index 20b8d287e8..58a317a960 100644 --- a/_pages/plugins/trackmate/detectors/trackmate-omnipose-advanced.md +++ b/_pages/plugins/trackmate/detectors/trackmate-omnipose-advanced.md @@ -7,7 +7,7 @@ categories: [Segmentation,Tracking,Machine Learning] artifact: sc.fiji:TrackMate-Cellpose --- -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-advanced-01.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-advanced-01.png" align='center' width='500' %} The advanced Omnipose integration in [TrackMate](/plugins/trackmate/index) works roughly as the [Omnipose integration](trackmate-omnipose). The omnipose advanced detector gives you the possibility to tune some Omnipose model hyper-parameters. It requires Omnipose to be installed on your system and working independently. This page gives installation details and advices at how to use the omnipose advanced integration in TrackMate. @@ -119,52 +119,50 @@ It is a relatively large image with a small pixel size (70 nm). Each 2D frame is 1824 x 1896 and a bacteria of length 4 um is imaged over about 50 pixels. With this image selected, run TrackMate (_Plugins > TrackMate_). -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-01.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-01.png" align='center' width='500' %} -In the detector selection panel, pick the **omnipose advanced detector**. +In the detector selection panel, pick the **Omnipose advanced detector**. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-advanced-tutorial-02.png" align='center' width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-advanced-tutorial-02.png" align='center' width='300' %} The configuration panel is quasi identical to that of the omnipose detector. Here, the default values will give us satisfactory results. -You just need to edit the path to the python executable on your computer. -For a windows computer where omnipose has been installed following the instructions above, this path is something like `C:\Users\tinevez\anaconda3\envs\omnipose-106\python.exe`. -For a Mac computer, the path will be like `/opt/miniforge/base/envs/omnipose-106/bin/python`. +You just need to set the conda environment to be the one where you installed Omnipose. -To change the values of the flow and mask thresholds, you need to slide their associated cursor in the panel. +The values of the flow and mask thresholds are set with two sliders and number inputs. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-advanced-tutorial-03.png" align='center' width='300' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-advanced-tutorial-03.png" align='center' width='300' %} Click the `Preview` button to check that the settings give correct results. Here the quality is equal to the number of pixels inside detected objects. We see that there are some very small objects with settings we have, but otherwise the results are excellent with the default parameters. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-advanced-tutorial-04.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-advanced-tutorial-04.png" align='center' width='500' %} We can now run the detection over the whole movie by clicking the `Next` button. On my windows machine with a NVIDIA 2080Ti, it takes about 3 to 4 seconds per frame. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-advanced-tutorial-05.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-advanced-tutorial-05.png" align='center' width='500' %} When done, click `Next` to reach the initial filtering panel. The quality histogram displays a small peak at low quality, corresponding to small spurious objects. We can filter them out by setting the threshold in between the two peaks in the histogram. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-06.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-06.png" align='center' width='300' %} We can skip filtering objects. Click `Next` until you reach the tracker selection panel. The bacteria do not move much, but divide, pushing each other away. We found out that for this kind of dynamics the **Overlap tracker** gives good tracking results. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-07.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-07.png" align='center' width='300' %} A few parameter needs to be tuned. For the `Min IoU` we use **0.1**. For the `Scale factor` we use **1.3**. Click `Next`. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-08.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-08.png" align='center' width='300' %} This yields a good lineage of the bacteria. We see each "colony" grows, and mix with the close ones. @@ -177,16 +175,16 @@ In the display settings window that appear, make the following changes: - for `spot color`, select **Track index** - uncheck `draw tracks` -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-09.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-09.png" align='center' %} The lineages that we see in the [TrackScheme](/plugins/trackmate/views/trackscheme) window show that for some bacteria, there might be some linking errors late in the movie. But overall we could correctly detect the division events over 5 generations in this movie. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-10.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-10.png" align='center' %} In the very last panel of TrackMate, an action called **Plot N spots vs time** highlights the exponential growth of the bacteria: -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-11.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-11.png" align='center' %} diff --git a/_pages/plugins/trackmate/detectors/trackmate-omnipose.md b/_pages/plugins/trackmate/detectors/trackmate-omnipose.md index 0d1916f522..e26cf47beb 100644 --- a/_pages/plugins/trackmate/detectors/trackmate-omnipose.md +++ b/_pages/plugins/trackmate/detectors/trackmate-omnipose.md @@ -2,12 +2,12 @@ title: TrackMate-Omnipose categories: [Segmentation,Tracking,Deep Learning] icon: /media/icons/omniposelogo.png -description: omnipose integration in TrackMate. +description: Omnipose integration in TrackMate. categories: [Segmentation,Tracking,Machine Learning] artifact: sc.fiji:TrackMate-Cellpose --- -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-01.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-01.png" align='center' width='500' %} The Omnipose integration in [TrackMate](/plugins/trackmate/index) works roughly as the [Cellpose integration](trackmate-cellpose) one. It requires Omnipose to be installed on your system and working independently. This page gives installation details and advices at how to use the omnipose integration in TrackMate. @@ -130,51 +130,53 @@ It is a relatively large image with a small pixel size (70 nm). Each 2D frame is 1824 x 1896 and a bacteria of length 4 um is imaged over about 50 pixels. With this image selected, run TrackMate (_Plugins > TrackMate_). -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-01.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-01.png" align='center' width='500' %} -In the detector selection panel, pick the **omnipose detector**. +In the detector selection panel, pick the **Omnipose detector**. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-02.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-02.png" align='center' %} The configuration panel is quasi identical to that of the cellpose detector. The list of pretrained models is however specific to omnipose. Also, since in TrackMate we give the size of objects to detect in microns, we can expect to use a `diameter` value comparatively smaller for bacteria. Here, the default values will give us satisfactory results. -You just need to edit the path to the python executable on your computer. -For a windows computer where omnipose has been installed following the instructions above, this path is something like `C:\Users\tinevez\anaconda3\envs\omnipose\python.exe` -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-03.png" align='center' %} +You just need to specify the name of the conda environment where Omnipose is installed. +(In the case below it is in the environment named _omnipose-jyt_). + +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-03.png" align='center' width='300' %} Click the `Preview` button to check that the settings give correct results. Here the quality is equal to the number of pixels inside detected objects. We see that there are some very small objects with settings we have, but otherwise the results are excellent with the default parameters. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-04.png" align='center' width='500' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-04.png" align='center' width='500' %} We can now run the detection over the whole movie by clicking the `Next` button. On my windows machine with a NVIDIA 2080Ti, it takes about 3 to 4 seconds per frame. +On a 2022 MacBook Pro, it takes about 10 minutes in total. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-05.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-05.png" align='center' width='400' %} When done, click `Next` to reach the initial filtering panel. The quality histogram displays a small peak at low quality, corresponding to small spurious objects. We can filter them out by setting the threshold in between the two peaks in the histogram. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-06.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-06.png" align='center' %} We can skip filtering objects. Click `Next` until you reach the tracker selection panel. The bacteria do not move much, but divide, pushing each other away. We found out that for this kind of dynamics the **Overlap tracker** gives good tracking results. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-07.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-07.png" align='center' %} A few parameter needs to be tuned. For the `Min IoU` we use **0.1**. For the `Scale factor` we use **1.3**. Click `Next`. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-08.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-08.png" align='center' %} This yields a good lineage of the bacteria. We see each "colony" grows, and mix with the close ones. @@ -187,16 +189,16 @@ In the display settings window that appear, make the following changes: - for `spot color`, select **Track index** - uncheck `draw tracks` -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-09.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-09.png" align='center' %} The lineages that we see in the [TrackScheme](/plugins/trackmate/views/trackscheme) window show that for some bacteria, there might be some linking errors late in the movie. But overall we could correctly detect the division events over 5 generations in this movie. -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-10.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-10.png" align='center' %} In the very last panel of TrackMate, an action called **Plot N spots vs time** highlights the exponential growth of the bacteria: -{% include img src="/media/plugins/trackmate/detectors/trackmate-omnipose-tutorial-11.png" align='center' %} +{% include img src="/media/plugins/trackmate/detectors/cellpose/trackmate-omnipose-tutorial-11.png" align='center' %} diff --git a/_pages/plugins/trackmate/tutorials/trackmate-segmentation-editor.md b/_pages/plugins/trackmate/tutorials/trackmate-segmentation-editor.md index 0f4aa7cf89..fc72373c2b 100644 --- a/_pages/plugins/trackmate/tutorials/trackmate-segmentation-editor.md +++ b/_pages/plugins/trackmate/tutorials/trackmate-segmentation-editor.md @@ -45,14 +45,14 @@ Otherwise, here is how to navigate in the image panel. - Rotate the image with the {% include key key="left click" %}. - Zoom in and out towards the center of the panel with the {% include key key="Mouse Wheel" %}. -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-07.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-07.gif" align="center" width="300" %} ### Resetting the view - {% include key key="Shift|Z" %} aligns the view with the XY plane. - {% include key key="Shift|R" %} resets the view. -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-08.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-08.gif" align="center" width="300" %} ### Navigating to spots @@ -61,7 +61,7 @@ Each label initially corresponds to a spot in TrackMate. The label will have the same name and color that of the current view in TrackMate. Shift-clicking on a label in the list will center the image view on the corresponding spot mask -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-09.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-09.gif" align="center" width="300" %} ### Changing the display settings @@ -69,7 +69,7 @@ The _Image_ section in the left side bar allows you to change the display settin The _auto contrast_ button will set the display range to the min and max pixel values in the image. The _settings_ button will open a dialog to change the display settings of the image, such as the color table, the brightness and contrast and the visibility of channels and spots. -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-10.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-10.gif" align="center" width="300" %} ## Editing spots @@ -121,7 +121,7 @@ The selected modes and brush size are remembered between use of the editor. This is the default mode. If you paint over an existing label, it is discarded. -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-Replace.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-Replace.gif" align="center" width="300" %} #### Paint add @@ -129,7 +129,7 @@ In TrackMate the spots can be overlapping, and as a consequence, in the editor y This is the way to edit overlapping spots. The _Add_ mode paint labels, and if there is an existing label, it will add the selected one, and not remove the existing one: -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-AddMode.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-AddMode.gif" align="center" width="300" %} This will lead to overlapping spots, as demonstrated in the animation above. @@ -140,7 +140,7 @@ The selected label will be applied only on the pixels that have no label already It will only paint on the background. This is useful if you have close objects that you know are not overlapping. -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-PreserveMode.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Paint-PreserveMode.gif" align="center" width="300" %} ### Deleting pixels - {% include key key="F3" %} @@ -152,13 +152,13 @@ The delete tool also has several modes, that are made to harness possibly overla This mode simply remove all labels at once. You get the background where you paint. -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Delete-AllLabels.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Delete-AllLabels.gif" align="center" width="300" %} #### Delete selected label With this mode, the brush will only remove the label currently selected, and no touch the others: -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Delete-SelectedLabel.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-Delete-SelectedLabel.gif" align="center" width="300" %} ### Flood fill - {% include key key="F4" %} @@ -173,7 +173,7 @@ There is also two modes for this tool. Similar to the _Paint add_ tool, this leads to replace all the label of a segment with the selected label. On the animation below, the red part of the labeling is overwritten with the blue label, leading to a larger spot: -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodFill-Replace.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodFill-Replace.gif" align="center" width="300" %} #### Flood fill add @@ -182,7 +182,7 @@ On the animation below, the red label is added to the blue segment. This segment has now two labels, the blue and the red, and it appers in magenta in the editor. After returning to TrackMate, this leads to two spots overlapping: -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodFill-Add.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodFill-Add.gif" align="center" width="300" %} ### Flood erase - {% include key key="F5" %} @@ -193,7 +193,7 @@ This tool removes one or all labels of a segment. All the labels are removed from the segment. The modified pixels are now part of the background. -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodErase-RemoveAll.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodErase-RemoveAll.gif" align="center" width="300" %} #### Flood erase selected label @@ -201,7 +201,7 @@ Only the currently selected label is removed. For instance, on the animation below, the top segment has two labels, red and blue, and appears in magenta. The flood erase tool is used to remove the blue label from it, which leaves the red label. -{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodErase-SelectedLabel.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/TrackMate-Editor-FloodErase-SelectedLabel.gif" align="center" width="300" %} ### Select label - {% include key key="F6" %} @@ -217,7 +217,7 @@ First you can press {% include key key="F1" %}, {% include key key="F2" %}, {% i You can also use {% include key key="Q" %} and , {% include key key="E" %} to decrease and increase the brush size, respectively. Pressing {% include key key="Shift|Q" %} and {% include key key="Shift|E" %} will change the brush size by a larger amount. -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-16.gif" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-16.gif" align="center" width="300" %} ### Keyboard shortcuts for editing @@ -245,7 +245,7 @@ For adding, deleting, removing and filling spots, the keys must be used as modif Once you are done editing, click the _Close and send to TrackMate_ button in the top left side bar. This will close the editor and prompt you with the following dialog: -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-17.png" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-17.png" align="center" width="300" %} If you click _No_, your changes will be discarded. If you click _Yes_, the modified spots will be sent to TrackMate. @@ -298,7 +298,7 @@ If the edits generate masks that are too different from the initial spots, they You can customize the key bindings of the spot editor in a dedicated dialog, displayed when you press {% include key key="Ctrl|," %}. -{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-21.png" align="center" %} +{% include img src="/media/plugins/trackmate/spot-editor/trackmate-spot-editor-tuto-21.png" align="center" width="400" %} ### Closing the editor window diff --git a/media/plugins/trackmate/trackmate-cellpose-biop-yaml-page.png b/media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-biop-yaml-page.png similarity index 100% rename from media/plugins/trackmate/trackmate-cellpose-biop-yaml-page.png rename to media/plugins/trackmate/detectors/cellpose/trackmate-cellpose-biop-yaml-page.png diff --git a/media/plugins/trackmate/trackmate-cellpose-convert-rgb-01.png 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based on a publication. If you use it successfully for your research please be so kind to cite our work: -rshov, D., Phan, MS., Pylvänäinen, J.W., Rigaud S.U., et al. TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nat Methods (2022). https://doi.org/10.1038/s41592-022-01507-1 -https://doi.org/10.1038/s41592-022-01507-1 -and / or: -Tinevez, JY.; Perry, N. & Schindelin, J. et al. (2017), 'TrackMate: An open and extensible platform for single-particle tracking.', Methods 115: 80-90, PMID 27713081. -https://www.sciencedirect.com/science/article/pii/S1046202316303346 - -Numerical feature analyzers: -Spot feature analyzers: - - Manual spot color provides: Spot color; is manual. - - Spot intensity provides: Mean ch1, Median ch1, Min ch1, Max ch1, Sum ch1, Std ch1. - - Spot contrast and SNR provides: Ctrst ch1, SNR ch1. - - Spot fit 2D ellipse provides: l. x0, l. y0, l. long axis, l. sh. axis, l. angle, l. a.r. - - Spot 2D shape descriptors provides: Area, Perim., Circ., Solidity, Shape index. -dge feature analyzers: - - Directional change provides: γ rate. - - dge speed provides: Speed, Disp. - - dge target provides: Source ID, Target ID, Cost. - - dge location provides: dge T, dge X, dge Y, dge Z. - - Manual edge color provides: dge color; is manual. -Track feature analyzers: - - Branching analyzer provides: N spots, N gaps, N splits, N merges, N complex, Lgst gap. - - Track duration provides: Duration, Track start, Track stop, Track disp. - - Track index provides: Index, ID. - - Track location provides: Track X, Track Y, Track Z. - - Track speed provides: Mean sp., Max speed, Min speed, Med. speed, Std speed. - - Track quality provides: Mean Q. - - Track motility analysis provides: Total dist., Max dist., Cfn. ratio, Mn. v. line, wd. progr., Mn. γ rate. - -Image region of interest: -Image data: -or the image named: Celegans-2D.tif. -Matching file Celegans-2D.tif in folder: /Users/tinevez/Development/Writing/imagej.github.io/media/plugins/trackmate/spot-editor/ -Geometry: - X = 0 - 239, dx = 0.198467 - Y = 0 - 294, dy = 0.198467 - Z = 0 - 0, dz = 1.00000 - T = 0 - 16, dt = 2.00000 - -Configured detector Thresholding detector with settings: - - target channel: 1 - - simplify contours: true - - intensity threshold: 1000.0 - -Starting detection process using 12 threads. -Detection processes 12 frames simultaneously and allocates 1 thread per frame. -ound 154 spots. -Detection done in 0.3 s. - -Computing spot quality histogram... -Initial thresholding with a quality threshold above -347.4 ... -Starting initial filtering process. -Retained 154 spots out of 154. - -Adding morphology analyzers... - - Spot fit 2D ellipse provides: l. x0, l. y0, l. long axis, l. sh. axis, l. angle, l. a.r. - - Spot 2D shape descriptors provides: Area, Perim., Circ., Solidity, Shape index. - -Calculating spot features... -Calculating features done in 0.1 s. - -Performing spot filtering on the following features: -No feature threshold set, kept the 154 spots. - -Configured tracker LAP Tracker with settings: - - max frame gap: 2 - - alternative linking cost factor: 1.05 - - linking feature penalties: - - linking max distance: 5.0 - - gap closing max distance: 15.0 - - merging feature penalties: - - splitting max distance: 5.0 - - blocking value: Infinity - - allow gap closing: false - - allow track splitting: true - - allow track merging: true - - merging max distance: 5.0 - - splitting feature penalties: - - cutoff percentile: 0.9 - - gap closing feature penalties: - -Starting tracking process. -Tracking done in 0.0 s. -ound 13 tracks. - - avg size: 9.8 spots. - - min size: 2 spots. - - max size: 37 spots. - -Calculating features done in 0.0 s. - -Performing track filtering on the following features: -No feature threshold set, kept the 13 tracks. - -Configured tracker LAP Tracker with settings: - - max frame gap: 2 - - alternative linking cost factor: 1.05 - - linking feature penalties: - - linking max distance: 5.0 - - gap closing max distance: 15.0 - - merging feature penalties: - - splitting max distance: 5.0 - - blocking value: Infinity - - allow gap closing: false - - allow track splitting: true - - allow track merging: true - - merging max distance: 5.0 - - splitting feature penalties: - - cutoff percentile: 0.9 - - gap closing feature penalties: - - -Configured tracker LAP Tracker with settings: - - max frame gap: 2 - - alternative linking cost factor: 1.05 - - linking feature penalties: - - linking max distance: 5.0 - - gap closing max distance: 15.0 - - merging feature penalties: - - splitting max distance: 5.0 - - blocking value: Infinity - - allow gap closing: false - - allow track splitting: true - - allow track merging: true - - merging max distance: 5.0 - - splitting feature penalties: - - cutoff percentile: 0.9 - - gap closing feature penalties: - -Starting tracking process. -Tracking done in 0.0 s. -ound 13 tracks. - - avg size: 9.8 spots. - - min size: 2 spots. - - max size: 37 spots. - -Calculating features done in 0.0 s. - -Performing track filtering on the following features: -No feature threshold set, kept the 13 tracks. -Saving data... -Computing edge features: - - Directional change in 1 ms. - - dge speed in 1 ms. - - dge target in 1 ms. - - dge location in 1 ms. -Computation done in 4 ms. -Computing track features: - - Branching analyzer in 0 ms. - - Track duration in 1 ms. - - Track index in 0 ms. - - Track location in 1 ms. - - Track speed in 1 ms. - - Track quality in 0 ms. - 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