Tools for making objects
PreviousAnalyzing image data with objects, connections, and propertiesNextTools for connecting objects
Last updated
Last updated
The way to create and edit objects is through the use of tools. Tools are defined by the user and are customized (via tags) to flexible organization of the results without a lot of clicking. There are two general categories of tools, manual and automated.
Manual tools. Manual tools are often based on a primitive type, like a point, line, or blob, and is customized with tags and other features. For instance, you might make a tool for circling cells in your image. You would choose a manual blob creation tool, name it "Nucleus" and add the tag [nucleus]. You could also add a hotkey. That tool gets added to your interface and you can use it to circle cells at any time.
Automated tools. NimbusImage has been designed to allow the use of automated algorithms for finding things like cells and points in your images, often using the latest deep learning methods. For instance, you can set up an automated tool to use Cellpose to find cells within your images. These tools often have specific parameters that you can use to obtain optimal results.
Manual blob/point/line/rectangle tools are the most basic tools in NimbusImage. Set it up by clicking on "Add New Tool" and choosing "Manual Blob", "Manual Point", "Manual Line", or "Manual Rectangle". Set the tag, and then you can use it to create objects in your image. You can use the same tag for multiple tools. For instance, you can use an automated cell finding tool and then add more cells using the manual tool, and they will be all treated the same for downstream analysis.
Segment-Anything is a new method for finding objects in images. It is a semi-automated tool that allows you to find objects in your images by either clicking on them or drawing a bounding box around them. To use it, click on "Add New Tool" and choose "ViT-B" under "Segment Anything Model".
Options include:
Simplification: The simplification parameter controls how much the segmentation is smoothed out. Smoother annotations are faster to run computations on and navigate.
Turbo mode: The turbo mode allows you to rapidly segment without having to manually "accept" each segment.
Segment Anything works by first encoding the image to enable it to find the objects. This will take a few seconds to compute when you move to a new area. Every time you move the image, you will need to re-encode the image; if you want to avoid this, click the "lock" icon at the bottom left of the image to prevent the image from moving around.
Once encoded, just move your mouse over the image and it will outline what the object would be if you were to shift-click. Shift-click to make the object! Sometimes, it will give you better results if you define a box around the object you want to find. Just shift-click and drag to make a box, and it will segment that region.
You can edit objects by using the Annotation Edits -> Blob edit tool. With that tool, you can just drag on your object and it will "slice" it into a new object. Whatever line you draw will define a new outline for that segment of the object. That allows you to both remove and add areas to the objects without having to use separate brush and eraser tools.
We have a number of automated tools for finding and connecting objects (cells, spots, etc.) that take advantage of the latest deep learning methods.
Cellpose-SAM combines the power of Cellpose with the Segment Anything Model (SAM) to offer a versatile approach to cell segmentation. It simplifies the input process by allowing users to specify up to three input channels, making it adaptable for various segmentation scenarios, from nuclei-only to full RGB images.
Cellpose-SAM processes images by taking input from up to three configurable channel "slots":
Single Channel Input:
To segment nuclei, provide the nuclear stain channel to Slot 1.
To segment cell boundaries, provide the cytoplasm/membrane channel to Slot 1.
Dual Channel Input: For improved cell boundary segmentation, you can provide the cytoplasm/membrane channel to Slot 1 and the nuclear channel to Slot 2.
Triple Channel Input (RGB): To segment cells in an RGB image, map the Red, Green, and Blue channels to Slots 1, 2, and 3 respectively.
The model then uses this information to perform segmentation.
Currently, the primary model available is:
cellpose-sam: This is the base model and should be suitable for most general-purpose cell segmentation tasks.
Model: Selects the segmentation model. Defaults to cellpose-sam.
Channel for Slot 1: Required. The primary channel for segmentation. Select the source channel for the model's first input. If multiple are selected, only the first will be used.
Channel for Slot 2: (Optional) The secondary channel, often used for nuclear information when segmenting cytoplasm. If multiple are selected, only the first will be used.
Channel for Slot 3: (Optional) The tertiary channel, typically used for the blue channel in RGB images. If multiple are selected, only the first will be used.
Diameter: The approximate diameter of the cells in pixels. While important for original Cellpose, Cellpose-SAM is less sensitive to this parameter. A value around 30 pixels often works well as a starting point, but can be adjusted (0-200 pixels, default 10).
Smoothing: Controls the simplification of the generated polygons (0-10, default 0.7). Higher values create smoother outlines. A value of 0.7 is a good default.
Padding: Expands (positive values) or contracts (negative values) the final polygons in pixels (-20 to 20 pixels, default 0).
Tile Size: The size of image tiles (in pixels) for processing (0-2048, default 1024). Larger tiles require more memory.
Tile Overlap: The fractional overlap between adjacent tiles (0-1, default 0.1). Ensure this overlap is larger than your largest cells (e.g., for 1024px tiles with 0.1 overlap, objects should be <102px).
Channel Selection:
For nuclei: Use your nuclear stain in Slot 1.
For cell boundaries: Use your cytoplasm/membrane channel in Slot 1. Consider adding a nuclear channel to Slot 2 for refinement.
For RGB images: Map R, G, B to Slots 1, 2, and 3.
Diameter Setting: Start with a diameter around 30 pixels. Adjust if necessary, but exact precision is less critical than with standard Cellpose.
Review Results: Always visually inspect segmentation and adjust parameters if needed.
Post-process: Utilize NimbusImage's manual editing tools to correct any segmentation errors.
Cellpose is a deep learning-based tool for automatically finding and segmenting cells in microscopy images. It has been trained on a large collection of diverse cell images, making it highly effective for many cell types without requiring additional training. Cellpose is a powerful starting point for cell segmentation, allowing you to quickly generate cell outlines that can be refined with NimbusImage's interactive tools.
NimbusImage includes several pre-trained Cellpose models:
cyto3: The most accurate for general cell segmentation (recommended default)
cyto2: An older model for cell segmentation
cyto: The original cell segmentation model
nuclei: Specifically trained for nuclear segmentation
Primary Channel: The main channel to use for segmentation
For cytoplasm segmentation (Cyto3): use your cytoplasm/membrane channel
For nuclear segmentation (Nuclei): use your nuclear stain channel (e.g., DAPI)
Secondary Channel (optional): A secondary channel to improve segmentation
For cytoplasm segmentation (Cyto3): you can add your nuclear channel
For nuclear segmentation (Nuclei): leave this blank (adding a secondary channel may decrease performance)
Diameter: The approximate diameter of cells in pixels
This parameter is crucial for good results
Set this as close as possible to the actual cell diameter in your images
Smoothing: Controls how much the cell outlines are simplified (0-10)
Higher values create smoother outlines with fewer vertices
Default of 0.7 works well for most images
Increase for smoother boundaries, decrease for more precise outlines
Very precise outlines can decrease performance, especially if you have a large number of objects
Padding: Expand or contract the final cell outlines in pixels
Positive values expand the outlines (useful if cells appear too small)
Negative values contract the outlines (useful if segmentation is too generous)
Default of 0 means no adjustment
Tile Size: The size of each image tile in pixels
Default of 1024 works well for most images
Decrease if you encounter errors
Larger tiles will require more memory and can cause Cellpose to crash
Tile Overlap: The fraction of overlap between adjacent tiles
Default of 0.1 (10% overlap) works well in most cases
Important: Make sure your overlap is larger than your largest cells
For example, for 1024 pixel tiles with 0.1 overlap, the largest cell should be less than 102 pixels across
Start with the right model: Use cyto3 for general cell segmentation or nuclei for nuclear segmentation, or use your own custom retrained model
Set the diameter carefully: This is the most important parameter for accurate results
Check results visually: Always inspect the segmentation and refine parameters if needed
Use the secondary channel when segmenting cytoplasm if you have a good nuclear stain
Post-process as needed: Use NimbusImage's manual editing tools to correct any segmentation errors
(Currently for Cellpose 3.0 only; Cellpose-SAM training coming soon!) NimbusImage offers the ability to train custom Cellpose models directly within the platform using your own annotated data. This feature allows you to create specialized segmentation models tailored to your specific cell types or imaging conditions. Custom models will often provide greatly improved performance for your specific dataset, even if your cells look pretty "normal", and especially if they don't!
To train a custom model, you'll need:
Representative images with cells you want to segment
Accurate cell annotations (outlines) created manually or corrected from automated results
Optional: Defined regions of interest for training
Base Model: The pre-trained model to use as a starting point
cyto3: Recommended for most cell types
nuclei: Use when training a nuclear segmentation model
Custom models: Previously trained models will also appear here
Nuclear Model?: Check this box if you're training a model for nuclear segmentation
When checked, the model will be optimized for nuclear detection
Output Model Name: Name for your custom model
This model will be saved and appear in the Cellpose worker's model list
Primary Channel: The main channel for segmentation
For cytoplasm models: use your cytoplasm/membrane channel
For nuclear models: use your nuclear stain channel
Secondary Channel: The secondary channel to improve segmentation
For cytoplasm models: your nuclear channel
For nuclear models: leave blank or set to -1
Training Tag: The tag applied to annotated cells used for training
All cells with this tag will be used to train the model
Use this to select high-quality annotations
Training Region: Optional tag (recommended) to define specific regions for training
If selected, only annotations within these regions will be used
Useful for limiting training to representative areas of your image
Unless you have a specific reason to change these, leave them at the default values.
Learning Rate: Controls how quickly the model adapts during training
Default: 0.01
Lower values (0.001) create more stable but slower training
Higher values can speed up training but might decrease stability
Epochs: Number of training iterations
Default: 1000
More epochs generally improve results but take longer
Consider increasing for complex cell types
Weight Decay: Regularization parameter to prevent overfitting
Default: 0.0001
Higher values create simpler models that may generalize better
Create high-quality annotations of your cells
Tag these annotations with a consistent label (e.g., "training_cells")
Ensure annotations are accurate and representative
Don't forget that what you do NOT annotate will also be used for training!
Optional: Define training regions
Create rectangles or polygons around areas with good examples
Tag these regions (e.g., "training_region")
Choosing specific regions can be very helpful in focusing the retraining on specific areas of your image
Add a Cellpose Training tool from the toolset menu
Configure all parameters as described above
Run the training process
Use your custom model
After training completes, your model will appear in the Cellpose worker's model list
Select it when using the regular Cellpose tool for segmentation
Provide diverse examples: Include cells of different sizes, shapes, and intensities
Quality over quantity: A smaller set of perfect annotations is better than many poor ones. A few good images, carefully annotated, will usually do the trick.
Choose representative regions: Select areas with good imaging quality and typical cells. Be sure to include examples of cells that the default algorithm might have trouble with.
Be patient: Training can take several minutes to complete
Your custom Cellpose models are saved in a .cellpose directory. You can:
Manually add models trained offline to this directory
Share models between team members by copying model files
Use these models in the standard Cellpose Python package
The integration between NimbusImage and Cellpose makes it easy to create specialized models for your specific research needs without requiring deep learning expertise.
Piscis is a deep learning-based tool for automatically finding and segmenting spots in microscopy images. It has been trained on a large collection of diverse spot images, making it highly effective for many spot types without requiring additional training. Piscis is a powerful starting point for spot segmentation, allowing you to quickly generate spot outlines that can be refined with NimbusImage's interactive tools. It can be retrained on your own data as well, which can often give you great results if the default models don't work well for your data.
Key tip: If you're getting too many or too few spots, try different models first before adjusting other parameters. The built-in models vary in sensitivity, and selecting the right one is usually more effective than tweaking threshold values.
Piscis uses a specialized neural network designed specifically for detecting small punctate structures in fluorescence microscopy images, such as:
RNA molecules in FISH experiments
Protein clusters
Vesicles
Small organelles
Synaptic puncta
The model can work in both 2D (single slice) and 3D (Z-stack) modes, making it flexible for different experimental setups.
Model: Select which pre-trained Piscis model to use
Different models have varying levels of sensitivity
Try several models if you're not getting optimal results
Custom trained models will also appear in this list
Mode: Choose between "Current Z" or "Z-Stack"
Current Z: Process each Z-slice independently (2D mode)
Z-Stack: Process the entire Z-stack as a 3D volume (better for connecting spots across Z)
Scale: Adjust for the size of spots (0-5)
Default value of 1 works well for most applications
Increase to detect larger spots
Decrease to detect smaller spots
Affects how the model interprets spot size relative to the training data
Threshold: Confidence threshold for spot detection (0-9)
Default value of 1.0 works for most cases
Note that this parameter has a relatively minor effect compared to changing models
Higher values create stricter detection (fewer spots)
Lower values create more lenient detection (more spots)
Skip Frames Without: Optional tag to skip processing frames
If specified, only process frames containing objects with the given tag
Useful for optimizing processing time by focusing on relevant frames
Batch processing: Process multiple positions, Z-slices, or time points
Use the "Batch XY", "Batch Z", and "Batch Time" fields to specify ranges
Format example: "1-3, 5-8" processes positions 1, 2, 3, 5, 6, 7, 8
Note: If using "Z-Stack" mode, the "Batch Z" field is ignored
Start with the default model: Try the default "20230905" model first
Try different models: If detection isn't optimal, switching models is more effective than adjusting thresholds
Use Z-Stack mode for 3D data where spots may span multiple Z-slices
Validate results visually: Always check the detection results manually
Consider retraining: For specialized applications, retraining on your own data can significantly improve results
Add a new Piscis tool from the toolset menu
Select your channel containing spots
Choose a model (start with the default)
Select either "Current Z" or "Z-Stack" mode
Run the model
Review the results
If needed, try a different model or adjust parameters and run again
If the pre-built models don't work well for your specific spot detection needs, consider training a custom model using the Piscis Train tool (documented separately). Custom-trained models will appear in the model selection dropdown once trained.
Piscis is particularly effective for RNA FISH and similar applications where automatic detection of small punctate structures is needed, saving significant time compared to manual annotation.
The Piscis Training tool allows you to create custom spot detection models tailored to your specific microscopy data. By training on your own annotated examples, you can significantly improve spot detection performance for challenging or unique datasets.
Key tip: You don't need extensive training data to get good results! Even a few carefully annotated regions can produce a highly effective custom model.
Consider training a custom Piscis model when:
Default models consistently detect too many or too few spots
Your spots have unusual characteristics (size, intensity, shape)
You're working with specialized microscopy techniques
Background noise or artifacts are causing detection issues
To train an effective custom model:
Create point annotations for the spots you want to detect
Tag them consistently (e.g., "training_spots")
Be thorough within your selected regions - mark all visible spots
Define training regions where your annotations are complete
Use rectangles or polygons to outline areas with fully annotated spots
Tag these regions consistently (e.g., "training_region")
Focus on high-quality regions rather than quantity
Initial Model Name: The base model to start training from
Starting from an existing model (like "20230905") speeds up training
The base model provides initial weights that get refined with your data
New Model Name: What to call your custom model
Default is a timestamp (e.g., "20250305_143022")
Consider using a descriptive name for your experiment or spot type
Annotation Tag: The tag used on your spot annotations
Only points with this tag will be used for training
Region Tag: The tag used on your training regions
Only annotations within these regions will be used for training
Crucial for ensuring the model learns from fully annotated areas
Learning Rate: Controls how quickly the model adapts (default: 0.2)
Higher values can lead to faster convergence but may be less stable
Lower values produce more stable but slower training
Weight Decay: Regularization parameter to prevent overfitting (default: 0.0001)
Higher values create simpler models that may generalize better
Epochs: Number of training iterations (default: 40)
More epochs generally improve results but take longer
The default is usually sufficient for good performance
Random Seed: Controls randomness in training for reproducibility (default: 42)
Change this value if you want to try different random initializations
Create point annotations
Manually mark spots in representative areas of your image
Apply a consistent tag (e.g., "training_points")
Define training regions
Draw rectangles or polygons around fully annotated areas
Apply a consistent tag (e.g., "training_region")
Configure the Piscis Train tool
Select a base model
Configure training parameters (or use defaults)
Specify your annotation and region tags
Run the training process
This will take several minutes depending on your data
The model will be saved with your specified name
Use your custom model
Your trained model will automatically appear in the Piscis tool's model list
Select it when using Piscis for spot detection
Quality over quantity: A few well-annotated regions are better than many poorly annotated ones
Include challenging examples: Include difficult spots in your training data
Use representative regions: Select areas that reflect the variability in your dataset
Start small: Begin with a small training set and evaluate before expanding
Iterative improvement: Train, test, correct annotations, and train again for best results
After training, test your model by:
Using the standard Piscis tool with your new model
Comparing results against manual annotations in regions not used for training
Adjusting the Scale parameter slightly if needed for fine-tuning
Custom-trained Piscis models often dramatically improve spot detection accuracy for specialized applications, saving significant time in your image analysis workflow.
If you use Segment Anything Model (SAM) in your research, please cite the .
If you use Cellpose-SAM in your research, please ensure you cite the relevant papers for both and the .
If you use Cellpose in your research, please cite the .
If you use Piscis in your research, please cite the .
