The SAMJ-IJ is a powerful Fiji plugin for annotating microscopy images using various versions of the Segment Anything Model (SAM). This README provides detailed instructions on how to use the plugin for image annotation. In this first version of the plugin, the SAMJ-IJ Annotator is delivered to annotate images through the usage of prompts. The plugin is designed to be user-friendly and efficient, allowing for easy and accurate image annotation for further analysis.
Note
This is an EARLY RELEASE, many more improvements are coming! Your valuable suggestions for enhancements are encouraged in the Issues section or on the image.sc forum.
Before you can annotate images using SAMJ-IJ, you need to install the plugin in Fiji:
- Install Fiji: If you haven't already, download and install Fiji.
Important
For MacOS users, if your Fiji instance is launched from the Downloads folder, SAMJ will not work! Move Fiji to another folder, Documents or Desktop, for example.
-
Install SAMJ Plugin: Open Fiji and navigate to
Help > Update.... In theManage update siteswindow, and look for an update site namedSAMJ, select it, click onApply and closeand thenApply changes. Finally restart Fiji.If you cannot find
SAMJamong the update sites list, click onAdd update site/Add unlisted site, writeSAMJin theNamefield andhttps://sites.imagej.net/SAMJ/in theURLfield. Click onApply and close, click onApply changesand restart Fiji.
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Open SAMJ-IJ Annotator: Start Fiji and navigate to
Plugins > SAMJ > SAMJ Annotatorto open the plugin.
The different models available to install can change over time as new models are added or removed. Up to this date, the models available for installation are SAM2 Tiny, SAM2 Small, SAM2 Large, EfficientSAM, and EfficientViTSAM-l2. All these models do not need the use of GPU but the CPU of your workstation can impact its performance.
Model references and github repositories can be found directly on the SAMJ plugin.
Warning
Users with a low-end computer are advised not to use the EfficientSAM model as it might take up to 10 minutes to load the first time, or the computer can even be frozen. The fastest and lightest model is EfficientViTSAM-L2, but low-resource machines might take up to 2-3 minutes to load the first time. Subsequent loading times will be much faster (~10s).
These are the steps to install a model:
- Open the SAMJ Annotator plugin as described above.
- Choose a SAM model from the list provided within the plugin.
- Click on the
Installbutton next to the selected model. - Wait for the installation process to complete. This may take some time, depending on the model size, your computer, and your internet connection.
Caution
Model installation times vary based on your machine's specifications, ranging from seconds to up to 20 minutes. Please be patient.
This video demonstrates the live installation of EfficientViTSAM-l1 on a Mac M1.
Once you have one model installed, you can start to annotate your images. Firstly, you will need to open you image in Fiji and then encode it by clicking the "Go!" button in the SAMJ Annotator plugin. This will encode your image so you can start annotating it.
There are two different ways to annotate your images: using manual prompts (Manual) or with prompts that are already given by a previous routine (Preset prompts), for example, in Fiji.
Once the image is encoded, to annotate, choose either rectangles or points to draw ROIs or mark points on the image. The annotations will be sent to the ROI Manager, where you can manage them. When you are done annotating, you can export the resulting mask.
Check this video to see how to annotate with manual prompts.
Again, after your image is encoded, you will be able to use preset prompts for previous routines or workflows. SAMJ only needs points in the image or ROIs in the ROI Manager to annotate the image. These ROIs or points can be created by any other plugin or routine in Fiji.
As an example, in this video you can see some points created through "Find Maxima" in Fiji and then used to annotate the image with SAMJ.
When doing your annotations, depending on the nature of your images and the final goal of your annotations, you can choose to return all ROIs of the image or only the largest ROI. This can be done by checking or unchecking the Only return largest ROI checkbox in the ROI Manager.
In this table you can see the difference between these two options over the same image.
| Only return largest ROI activated | Only return largest ROI NOT activated |
|---|---|
 |
 |
This button simplifies the process of exporting your annotations, which are saved as semantic annotations where each marked region is assigned a distinct value. For enhanced visual clarity, we suggest altering the Look-Up Table (LUT) in Fiji when necessary(Image > Lookup Tables > Glasbey or choose another option).
This Fiji plugin is intended to work with microscopy images. To show its versatility among different images, here are some use cases.
The original image (top left in the figure) displays astrocytes stained for actin following mechanical deformation as part of a study exploring the mechanical and functional responses of astrocytes using magneto-active substrates [1]. The annotated image (bottom left in the figure) highlights individual astrocytes for detailed analysis. This annotation was accomplished using the "Points Prompt" feature coupled with the "Return Only Largest ROI" option to annotate each astrocyte visible in the image selectively. The primary goal of this annotation is to facilitate a comparative study of astrocyte morphology pre- and post-deformation, thus contributing valuable insights into the biomechanical properties and adaptive responses of astrocytes under stress.
The images (top center and bottom center in the figure) showcase the results of mobility assays for Pseudomonas aeruginosa strains on agar plates [2]. These assays are crucial for studying the surface motility of bacteria, which is considered a key factor in pathogenicity due to its role in chemotaxis, biofilm formation, and overall virulence. The original images depict the spread of bacteria on agar plates following incubation, captured using the Chemi DOC™ image system. The annotations made using the SAMJ plugin allow for precise measurement and analysis of the spread area, significantly automating a task that was previously manual, tedious, and time-consuming. By leveraging SAMJ for these annotations, researchers can efficiently quantify bacterial motility, facilitating deeper insights into bacterial behavior and its implications on disease spreading and antimicrobial resistance. This enhances the plugin's value in microbial research, providing a robust tool for assessing bacterial dynamics in a consistent and reproducible manner.
The images (top right and bottom right in the figure) illustrate organoids captured for the purpose of segmentation, counting, and analysis of morphological features such as area and eccentricity [3]. These organoids are typically used to model biological processes in vitro, providing a robust platform for studies in developmental biology, disease pathology, and drug screening. The original images capture the diverse shapes and sizes of organoids, which can be challenging to quantify manually. Using the SAMJ plugin, researchers can automate the segmentation and counting of organoids, and accurately measure their area and eccentricity. This annotation capability not only enhances the precision and efficiency of the analysis but also supports high-throughput screening and detailed morphometric assessments. The ability of SAMJ to handle such complex image data demonstrates its utility in advanced biological research and experimental reproducibility.
[1] Gomez‐Cruz, C., Fernandez‐de la Torre, M., Lachowski, D., Prados‐de‐Haro, M., del Río Hernández, A. E., Perea, G., ... & Garcia‐Gonzalez, D. (2024). Mechanical and Functional Responses in Astrocytes under Alternating Deformation Modes Using Magneto‐Active Substrates. Advanced Materials, 2312497.
[2] Casado-Garcia, A., Chichón, G., Dominguez, C., Garcia-Dominguez, M., Heras, J., Ines, A., ... & Saenz, Y. (2021). MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images. Computers in biology and medicine, 136, 104673.
[3] Segmentation, counting, and measurement of area and eccentricity (circularity) of organoids in image.sc forum
Carlos García-López-de-Haro, Bioimage Analysis Unit, Institut Pasteur, Université Paris Cité, Paris, France - @carlosuc3m
Caterina Fuster-Barceló, Bioengineering Department, Universidad Carlos III de Madrid, Leganés, Spain - @cfusterbarcelo
Curtis T. Rueden, Center for Quantitative Cell Imaging, University of Wisconsin, Madison, USA - @ctrueden
Jónathan Heras, Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain - @joheras
Vladimir Ulman, IT4Innovations, VSB - Technical University of Ostrava, Ostrava, Czech Republic - @xulman
Adrián Inés, Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain - @adines
Kevin Eliceri, Center for Quantitative Cell Imaging, University of Wisconsin, Madison, USA - @eliceiri
J.C. Olivo-Marin, CNRS UMR 3691, Institut Pasteur, Paris, France
Daniel Sage, Biomedical Imaging Group and Center for Imaging, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland - @dasv74
Arrate Muñoz-Barrutia, Bioengineering Department, Universidad Carlos III de Madrid, Leganés, Spain - @arratemunoz
- This plugin is intended to use with microscopy images.
- The documentation here is for users only. Developer documentation, including contribution guidelines, will be available in a separate repository.
- For further assistance or to report issues, please visit the plugin's repository.
Thank you for using the SAMJ-IJ Fiji plugin!
