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Training with StarDist Models | Image-Pro AI Deep Learning

Science & Technology

Introduction

In this article, we will explore how to train a StarDist deep learning model to detect a specific class of objects using Image-Pro AI. This process is simplified when utilizing pre-trained models, which can be adapted to suit your unique samples.

Considerations Before Training

Before diving into model training, consider the following steps:

  1. Check for Pre-trained Models: Always search for existing pre-trained models that closely resemble your samples. If available, load the model and evaluate its performance on your images. If the model effectively identifies your objects of interest, you can modify its parameters for better segmentation.

  2. Model Cloning: If a satisfactory pre-trained model exists, clone it rather than starting from scratch. This is particularly useful for refining the model with additional training.

  3. Create a New Model: If no pre-trained model meets your needs, you can create and train a new model from the ground up.

For this demonstration, we will utilize images from a mouse testis tissue sample and focus on segmenting sperm cells. The initial goal is to achieve precise segmentation before proceeding with cell classification.

Step-by-Step Training Process

1. Model Selection

  • Start by selecting the Count Size tab and then the Model Manager.
  • Once the Model Manager appears, browse through the available pre-trained models. Under the Life Science Optical category, select the "Hematoxylin Nuclei" model to test.
  • Load the selected model in the AI Deep Learning Prediction Panel.

2. Image Preparation and Configuration

  • Load your specific image into the prediction panel. Configure the class color to Yellow for clearer visibility of the nuclei outlines.
  • Adjust other parameters, such as line width and object diameter, to suit the average size of your cells. Utilize the size guide display to gauge size appropriately.

3. Testing and Adjusting the Model

  • Begin the segmentation by pressing the Count button. Review the segmentation results, noting any missed or falsely identified cells.
  • Adjust parameters like the object diameter or threshold to improve segmentation accuracy.

4. Cloning the Model for Additional Training

  • If further training is necessary, select the Clone Model option at the bottom of the AI Deep Learning Prediction Panel. This retains most existing settings and allows for easier retraining.
  • Provide a name and description for your cloned model and add any relevant tags for future reference.

5. Training the Model

  • Load the model into the AI Deep Learning Trainer panel. Select regions to label, ensuring complete and accurate annotation of all objects.
  • Once labeled, these objects can be added to the training set. Confirm that all relevant labels have been included.

6. Running the Training Process

  • Start the training session. The training graph will display progress, with the accuracy indicated by the loss rate.
  • After a sufficient number of steps, check the graph for plateauing, which suggests optimal training.

7. Testing the New Model

  • Following training, test the model on a new image. If necessary, make additional edits and re-add to the training set for improved performance.

Review and Application

  • After closing the model, return to the Count Size tool to manage and access your new model. It can be utilized for future predictions and incorporated into protocols.

For any inquiries or advanced support, please contact Minia Cybernetics for expert assistance.

Keywords

  • StarDist
  • Deep Learning
  • Image-Pro AI
  • Model Training
  • Segmentation
  • Pre-trained Models
  • Clone Model
  • Tissue Sample
  • Sperm Cells

FAQ

  1. What is StarDist?

    • StarDist is a deep learning model specifically designed for segmenting and detecting objects within images.

  2. Why should I use a pre-trained model?

    • Pre-trained models can save time and resources, providing a strong initial framework that can be fine-tuned to your specific objects of interest.

  3. How do I clone a model?

    • Within the AI Deep Learning Prediction Panel, select the "Clone Model" option, which retains existing parameters for easier adjustments.

  4. What should I do if my model isn't performing well?

    • Adjust parameters like object diameter and threshold, add more labeled training data, and consider retraining the model.

  5. Can I use the trained model in different protocols?

    • Yes, once trained, the model can be easily applied in various imaging protocols for consistent results.