Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities

Area of Science:

• Scientific user facilities
• Data science
• Image processing

Background:

• Scientific user facilities generate massive datasets, posing significant image processing challenges.
• Real-time analysis and artifact correction require computationally intensive algorithms.
• Traditional image segmentation methods struggle with complex, low-contrast scientific data.

Purpose of the Study:

• To address challenges in scientific image processing and data analysis.
• To accelerate the development and deployment of machine learning models for image segmentation.
• To provide an accessible platform for researchers to analyze large experimental and simulation data.

Main Methods:

• Developed MLExchange, a Machine Learning framework with interactive web interfaces.
• Integrated tools for data upload, visualization, labeling, and network training.
• Implemented a web-based application for training, testing, and evaluating machine learning models on tomography data.

Main Results:

• MLExchange facilitates interactive training and deployment of machine learning models.
• The platform allows for sharing of results and trained models among scientists.
• Users can intuitively segment images using various machine learning and deep learning algorithms.

Conclusions:

• MLExchange enhances and accelerates scientific data analysis through machine learning.
• The framework overcomes limitations of traditional image segmentation, especially for complex datasets.
• Interactive web interfaces democratize advanced image analysis for scientific research.