2025-10-02 –, Coffee room
Language: English
MedEye3d.jl is a vital Julia package for 3D medical image visualization, playing a crucial role in workflows from registration to segmentation analysis. This talk introduces recent advancements in MedEye3d.jl, stemming from GSoC, that push it towards a "digital twin" for medical imaging. We'll explore the implementation of a novel multi-image display mode, enabling concurrent visualization of registered heterogeneous studies (e.g., CT and PET). A key focus will be the introduction of dynamic supervoxel border rendering and interactive highlighting across these linked views, allowing for intuitive comparison and analysis. We'll also touch upon new features for interactive supervoxel association correction and control point annotations, significantly enhancing the tool's utility for registration, orientation, and detailed visual inspection. These enhancements leverage Julia's performance and ModernGL.jl for a responsive and powerful user experience, further solidifying Julia's position in the medical imaging domain.
The JuliaHealth ecosystem provides a powerful suite of tools for medical imaging, and MedEye3d.jl stands as a cornerstone for visual inspection throughout the processing pipeline. This presentation will detail the significant enhancements made to MedEye3d.jl during Google Summer of Code, transforming it into a more powerful and interactive "digital twin" for researchers and clinicians.
Motivation: the need for richer, comparative visualization capabilities, particularly for multi-modal datasets and superpixel/supervoxel-based analyses.
The core of the talk will cover the key new features:
- Advanced Multi-Image Supervoxel Visualization:
Demonstration of the new side-by-side panel view for different imaging modalities (e.g., CT alongside PET), a step up from previous same-modality views.
Technical insights into dynamic texture creation and fragment shader management for heterogeneous studies using ModernGL.jl.
Showcasing dynamically updated supervoxel borders that correspond to the current slice in both linked image views, with synchronized scrolling.
Highlighting corresponding supervoxels across images based on cursor position, facilitating direct anatomical comparison.
- Interactive Supervoxel Associations & Control:
Introduction of user interactions (via GLFW callbacks) to manually correct or confirm supervoxel associations between the displayed images.
Discussing the mechanism for users to flag incorrect default mappings and how MedEye3d.jl updates these associations, improving the tool's reliability through a user feedback loop.
- Enhanced Annotation: Control Points for Registration & Orientation:
Implementing control point annotations in multi-image mode, allowing users to mark corresponding anatomical landmarks across studies.
Detailing the persistence of these annotations, including support for exporting them (e.g., in NIfTI or JSON sidecar files) to aid subsequent image registration processes or for maintaining orientation context.
Throughout the presentation, we will emphasize how these features contribute to a more seamless and intuitive visual inspection process, crucial for detecting anomalies, verifying registration accuracy, and understanding complex spatial relationships in 3D medical data. We will also briefly touch upon efforts to improve package health, including documentation and testing, to foster community growth around MedEye3d.jl. The talk aims to showcase the practical power of Julia for developing sophisticated scientific visualization tools.
I engage with scientific computing and computational biology through Julia lang development, with applications in High-Energy Physics, Cryo-ET, and HPC. This includes two Google Summer of Code projects with The Julia Language (2024 & 2025). I am also an O'Reilly DEIJ Scholarship Recipient and a Microsoft Learn Student Ambassador.