[3.10-GC] Implement High-Performance WebGPU-Based 3D Grow Cut Segmentation Toolkit

[sedghi]

Purpose
This feature introduces a high-performance, WebGPU-accelerated 3D Grow Cut segmentation tool, designed for fast and efficient segmentation of large image volumes. By leveraging WebGPU, this tool provides rapid processing and supports both manual and automatic segmentation modes, giving users flexibility to control segmentation boundaries or opt for a streamlined, one-click segmentation experience.

Why This Matters
3D segmentation is a computationally intensive process, especially with high-resolution medical imaging data. Using WebGPU for parallel processing dramatically increases segmentation speed, making advanced analysis more accessible and less time-consuming. This tool is particularly beneficial in contexts like PET imaging, where rapid, precise segmentation is critical for identifying regions of interest.

Key Changes

• WebGPU Acceleration: Uses WebGPU to accelerate 3D Grow Cut segmentation, improving performance for large datasets.
• Manual and Automatic Modes: Offers a manual mode for user-defined segmentation and an automatic, one-click mode for quick, easy region identification.
• Configurable Segmentation Options: Allows users to set initial positive and negative regions for precise control over the segmentation process.

Impact on Users and Developers

• For Users: This tool provides a faster, more responsive 3D segmentation experience with flexible options for detailed control or quick, automated segmentation, enhancing usability across different workflows.
• For Developers: WebGPU integration offers a high-performance solution for handling large imaging datasets, making it easier to develop fast, efficient segmentation tools and potentially extend WebGPU-based processing capabilities in other areas.