Interactive MRI Segmentation with Controlled Active Vision.

Area of Science:

• Medical Imaging
• Computer Vision
• Control Theory

Background:

• Automated segmentation of Magnetic Resonance Imaging (MRI) data into anatomical structures remains challenging.
• Implicit functions are widely used for modeling structure boundaries and are compatible with control-theoretic approaches.

Purpose of the Study:

• To develop an interactive system for rapid and effortless accurate medical image segmentation.
• To frame the segmentation problem as a control synthesis task, integrating human input with automated methods.

Main Methods:

• Utilized implicit functions and control synthesis to model anatomical boundaries.
• Employed a Lyapunov control design to balance data-driven gradient flow with user input.
• Developed an observer-like system to integrate user interactions into the segmentation process.

Main Results:

• Demonstrated a method for smoothly coupling automatic segmentation with user interactivity.
• Showcased the application of these mathematical methods to orthopedic segmentation.
• Observed expected transient and steady-state behaviors in the implicit segmentation function and auxiliary observer.

Conclusions:

• The proposed control-theoretic approach enables efficient and accurate interactive medical image segmentation.
• This method effectively merges human guidance with automated image analysis for improved segmentation outcomes.
• The technique shows promise for applications in medical imaging, particularly in orthopedics.