Building a Egg Cell Detection System using Labellerr and YOLO

In the fields of biotechnology and healthcare, progress is often measured one cell at a time. Scientists spend countless hours peering through microscopes, manually identifying, counting, and tracking microscopic entities like egg cells.

From our experience in data science, we've seen firsthand that this manual process, while foundational, is a significant bottleneck. It's not just slow; it's prone to human fatigue and inconsistency, which can impact research outcomes.

The need for a faster, more reliable, and scalable solution is clear. This is where expert systems, in the form of artificial intelligence, demonstrate their profound value.

By building authoritative datasets and models, we can create a trustworthy workflow that empowers researchers, automating the mundane and accelerating the pace of discovery. This post explores how we can build such a system.

The Solution

Computer vision (CV) offers a powerful solution to this challenge. By using deep learning models, we can train a system to "see" and interpret microscopic footage just as a trained scientist would, but at a fraction of the time and with tireless precision.

The core of this solution is an object segmentation model, such as a YOLO (You Only Look Once) model. This model can be trained to perform several tasks in real-time:

• Detection: Instantly identify and locate every egg cell in a video frame.
• Segmentation: Go beyond a simple box and outline the exact shape of each cell.
• Tracking: Follow individual cells as they move, crucial for behavioral analysis.
• Analytics: Once cells are tracked, the system can automatically calculate advanced metrics, such as the distance between cells or their speed.

This automated approach transforms a time-consuming manual task into an efficient, data-rich analysis, allowing researchers to focus on results, not on the counting.

How Labellerr helps Biotechnology?

The most significant challenge in building a high-performance CV model is acquiring a large, accurately labeled dataset. A model is only as smart as the data it's trained on. This is especially difficult in specialized fields like biotechnology.

A platform like Labellerr is designed to solve this exact problem by streamlining the data annotation workflow. Instead of requiring experts to manually label thousands of individual video frames, Labellerr enables a "smart-labeling" process:

1. Annotate One Frame: A researcher uses the platform's tools to meticulously annotate just one representative frame from a video, identifying the target cells.
2. Automate with AI: The platform then employs an AI-powered feature, like a SAM (Segment Anything Model) tracker, to automatically apply those labels across the entire video. This propagates the annotations, tracking the cells from frame to frame.
3. Create a Dataset: In minutes, a single annotated frame is transformed into a complete, ready-to-use dataset containing hundreds or even thousands of labeled images.
4. Export for Training: This new dataset is then exported in a standard format (e.g., JSON), ready to be fed directly into the training pipeline for the YOLO model.

This process dramatically reduces the time and expertise needed for data preparation, making state-of-the-art AI accessible for highly specific use cases like microscopic cell detection.

Creating Egg Cell Detection Model using labellerr

While the platform handles the data, the following code structure outlines how you can use the exported data to train your own YOLO segmentation model.

This workflow assumes you have exported your annotation files and video from the annotation platform.

Check Cookbook

# 1. Setup & Data Conversion
# First, you would clone a utility repository containing helper functions
# (e.g., YOLO_fine_tunes.utilities)

# Import the necessary converter to change your annotation format
# (e.g., from COCO JSON) to the format YOLO expects.
from yolo_finding_utilities.video_annotation_converter import coco_to_yolo_segmentation

# Define the paths for your data
# This is the annotation file you exported from the platform
annotation_file = '/path/to/your/annotation.json'
  
# This is the directory containing your video frames
video_directory = '/path/to/your/video_frames/'
  
# This is where the converted YOLO dataset will be saved
yolo_dataset_directory = '/path/to/yolo_dataset/'

# Run the conversion function
coco_to_yolo_segmentation(
    json_path=annotation_file,
    video_dir=video_directory,
    save_dir=yolo_dataset
)

# 2. Model Training
# Ensure you have the 'ultralytics' package installed
from ultralytics import YOLO

# Load a pre-trained YOLO segmentation model
model = YOLO('yolov8n-seg.pt')

# Start the training process
# The 'data.yaml' file is created by your conversion script
# in the 'yolo_dataset'
results = model.train(
    data=f'{yolo_dataset}/data.yaml',
    epochs=150,       # Number of training cycles
    batch=-1,         # Automatically determines the best batch size
    device=0,         # Use the first available GPU
    workers=4         # Number of data loaders
)

# 3. Inference and Analysis
# Once trained, you can run inference on a new video
# The trained weights will be in a 'runs/...' folder
trained_model = YOLO('runs/segment/train/weights/best.pt')

# Run prediction
# You can also create a custom class to add features like
# random-colored tracking and distance calculation between cells.
inference_results = trained_model.predict(
    source='/path/to/new/test_video.mp4',
    save=True
)

print("Training and inference complete.")

Conclusion

The power of computer vision to revolutionize biotechnology is undeniable. However, these advanced AI models are not magic.

Their ability to perform complex tasks like cell segmentation with high accuracy is built on a simple, critical foundation: high-quality, precisely annotated data.

The model training workflow are essential, but without a clean, reliable dataset, the model will fail. The importance of the annotation step cannot be overstated. Platforms and workflows that accelerate and improve the quality of data labeling are, therefore, just as crucial as the models themselves. They are the essential bridge between a brilliant scientific idea and a functional, world-changing AI system.

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