3D MRI brain segmentation - Made with TensorFlow.js
Science & Technology
Introduction
In this article, we explore an innovative tool developed by Mohammad Masood and his research team at Georgia State University, designed for 3D MRI brain segmentation. This tool leverages TensorFlow.js to perform real-time segmentation directly in the browser, making it accessible to a wide range of users, primarily in the medical field.
Introduction to the Research Team
Mohammad Masood, who holds a PhD in Computer Science from Georgia State University, has developed a strong foundation in image processing and machine learning. After completing his PhD, he joined Emory University School of Medicine as a research associate before progressing further at Georgia City University’s Center for Neural Imaging and Data Science. Here, he collaborated with his team to create an advanced 3D segmentation tool that can dynamically segment MRI data.
Demonstrating the Tool: Printshop.org
The tool is accessible at printshop.org and features a straightforward interface that allows users to visualize original MRI data alongside segmentation results. The tool primarily focuses on segmenting gray matter (the outer regions of the brain) and white matter (the inner regions consisting of nerve fibers).
To use the tool, users can upload their data in the NIfTI file format and select from a list of available models to conduct various tasks such as plane extraction and segmentation. The tool processes the raw data in three stages:
- Pre-processing,
- Inference using the chosen model,
- Post-processing to eliminate any noise from the segmentation output.
Results are typically generated in about 25 to 30 seconds, allowing users to evaluate their effectiveness quickly. There’s also functionality for users to save their satisfactory results.
Target Users
The end users of this innovative tool primarily include medical professionals and doctors who may not be well-versed in machine learning. The tool’s simple interface allows them to view the accuracy of the models' outputs and make informed decisions about their research without needing deep technical knowledge.
Technical Framework
The segmentation model utilized in this tool is a lightweight variant. Initially, models are trained using frameworks such as PyTorch or Keras and then converted to TensorFlow.js. The core architecture is based on MishNet—a lightweight deep learning model that offers competitive performance compared to more traditional, heavier models like U-Net. MishNet is not only fast and efficient but also markedly smaller in size, which allows it to run effectively in a browser without overwhelming system resources.
Future Directions
Looking forward, the research team plans to expand the model zoo to include additional models for more detailed segmentation of brain regions. They are also optimistic about the potential applications of TensorFlow.js in other areas, such as histology and microscopy, bolstering machine learning's accessibility and privacy while enhancing its scalability in medical imaging.
The team’s work has been funded in part by the NIH, and their project aligns with the goals of the No Planner project. The open-source nature of the tool and its availability on GitHub ensures that others can leverage, adapt, and build upon this research.
For those interested in experimenting with this groundbreaking tool, more information about the demo and project repository is available on GitHub, with links provided in the description.
Conclusion
3D MRI brain segmentation using TensorFlow.js represents a significant advancement in medical imaging technology, offering fast, accessible, and efficient solutions for everyday use in various healthcare settings. Mohammad Masood and his team continue to explore new possibilities to enhance and broaden the functionality of this exciting tool.
Keywords
3D MRI brain segmentation, TensorFlow.js, image processing, MishNet, white matter, gray matter, machine learning, medical imaging, real-time segmentation.
FAQ
Q: What is the purpose of the 3D MRI brain segmentation tool?
A: The tool is designed to perform real-time segmentation of brain MRI data, focusing on distinguishing between gray matter and white matter efficiently in the browser.
Q: How can users access the tool?
A: Users can access the tool at printshop.org and utilize it by uploading their NIfTI format MRI data.
Q: What models does the tool use for segmentation?
A: The tool employs a lightweight model, MishNet, which is optimized for performance in a browser environment while maintaining competitive accuracy compared to heavier models.
Q: Who are the target users of this tool?
A: The primary users include medical professionals and doctors who benefit from a simple interface without needing extensive knowledge in machine learning.
Q: Can users expect future developments for this tool?
A: Yes, the research team plans to enhance the model zoo and expand the range of segmentation capabilities to provide even more detailed insights into MRI data.