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Interactive Manipulation and Visualization of 3D Brain MRI for Surgical Training

Siddharth Jha, Zichen Gui, Benjamin Delbos, Richard Moreau, Arnaud Leleve, Irene Cheng

TL;DR

The paper tackles the challenge of making MRI data more interpretable by combining automatic segmentation, 3D surface reconstruction, and interactive visualization into a single workflow. It employs SynthSeg for robust segmentation, Flying Edges via 3D Slicer for fast surface reconstruction, and a Three.js-based web interface for 2D and 3D visualization, all coordinated through Django and SQLite to minimize user input and enable result caching. The approach yields a standalone, extensible platform suitable for medical education, surgical planning, and collaborative data analysis, with doctors reporting good usability and clinical relevance while suggesting enhancements such as path planning and patient-facing AI support. Overall, the work demonstrates a practical, accessible framework that bridges advanced imaging techniques with interactive visualization to improve MRI interpretability and decision support in clinical settings.

Abstract

In modern medical diagnostics, magnetic resonance imaging (MRI) is an important technique that provides detailed insights into anatomical structures. In this paper, we present a comprehensive methodology focusing on streamlining the segmentation, reconstruction, and visualization process of 3D MRI data. Segmentation involves the extraction of anatomical regions with the help of state-of-the-art deep learning algorithms. Then, 3D reconstruction converts segmented data from the previous step into multiple 3D representations. Finally, the visualization stage provides efficient and interactive presentations of both 2D and 3D MRI data. Integrating these three steps, the proposed system is able to augment the interpretability of the anatomical information from MRI scans according to our interviews with doctors. Even though this system was originally designed and implemented as part of human brain haptic feedback simulation for surgeon training, it can also provide experienced medical practitioners with an effective tool for clinical data analysis, surgical planning and other purposes

Interactive Manipulation and Visualization of 3D Brain MRI for Surgical Training

TL;DR

The paper tackles the challenge of making MRI data more interpretable by combining automatic segmentation, 3D surface reconstruction, and interactive visualization into a single workflow. It employs SynthSeg for robust segmentation, Flying Edges via 3D Slicer for fast surface reconstruction, and a Three.js-based web interface for 2D and 3D visualization, all coordinated through Django and SQLite to minimize user input and enable result caching. The approach yields a standalone, extensible platform suitable for medical education, surgical planning, and collaborative data analysis, with doctors reporting good usability and clinical relevance while suggesting enhancements such as path planning and patient-facing AI support. Overall, the work demonstrates a practical, accessible framework that bridges advanced imaging techniques with interactive visualization to improve MRI interpretability and decision support in clinical settings.

Abstract

In modern medical diagnostics, magnetic resonance imaging (MRI) is an important technique that provides detailed insights into anatomical structures. In this paper, we present a comprehensive methodology focusing on streamlining the segmentation, reconstruction, and visualization process of 3D MRI data. Segmentation involves the extraction of anatomical regions with the help of state-of-the-art deep learning algorithms. Then, 3D reconstruction converts segmented data from the previous step into multiple 3D representations. Finally, the visualization stage provides efficient and interactive presentations of both 2D and 3D MRI data. Integrating these three steps, the proposed system is able to augment the interpretability of the anatomical information from MRI scans according to our interviews with doctors. Even though this system was originally designed and implemented as part of human brain haptic feedback simulation for surgeon training, it can also provide experienced medical practitioners with an effective tool for clinical data analysis, surgical planning and other purposes
Paper Structure (20 sections, 2 figures, 1 table)

This paper contains 20 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Segmentation
  4. Reconstruction
  5. Methodology
  6. Segmentation
  7. Reconstruction
  8. Visualization
  9. 2D Scan
  10. 3D Model
  11. Integration
  12. Results and Discussion
  13. Visual Representations
  14. Doctor Feedback
  15. Usability
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Workflow of the proposed pipeline.
  • Figure 2: The interface of proposed system displaying with 2D and 3D viewers for MRI visualization.