Medical Image Visualization Using 3D Mask Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing medical imaging techniques struggle to provide accurate and simplified three-dimensional representations of anatomical structures, as conventional methods like polygonal surface models fail to visualize the interior of organs and require significant data and can lead to inaccurate visualizations.
Innovation Solution
A method involving segmentation of medical image data into predetermined classes, generating a 3D mask, and applying a beam scanning technique to shift segmented volume elements based on a translation vector, allowing for the visualization of internal structures without intermediate representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If polygonal surface models are used to represent anatomical details, then individual structures can be modeled and moved independently, but the interior of anatomical structures cannot be examined and significant data is required
Solution Approach 1:
The volume data is segmented into different anatomical structures using classification algorithms. Each structure is assigned a unique class label, allowing independent manipulation while maintaining the original volumetric data integrity. This enables selective movement and examination of individual structures without requiring separate surface models for each.
Solution Approach 2:
The patent transitions from traditional 2D slice data to 3D volumetric representation with an additional classification dimension. By assigning class labels to voxels in three-dimensional space, the system enables both surface and interior examination simultaneously, eliminating the need for separate surface models.
2Ease of operation
If polygonal surface models are used to represent anatomical details, then individual structures can be moved independently, but accurate visualization is compromised and intermediate representations are required
Solution Approach 1:
The volume data is segmented into different anatomical structures using classification algorithms. Each structure is assigned a unique class label, allowing independent manipulation while maintaining the original volumetric data integrity. This enables selective movement and examination of individual structures without requiring separate surface models.
Solution Approach 2:
Instead of creating intermediate surface model copies that lose interior information, the system works directly with the original volumetric data. Classification labels are applied to the volume voxels themselves, allowing accurate representation without loss of detail or creation of intermediate representations.
3Loss of information
If the entire volume data is visualized, then complete anatomical context is provided, but detailed examination of specific areas is difficult
Solution Approach 1:
The system applies local quality by allowing different visualization parameters and levels of detail for different regions of the volume data. Specific areas can be highlighted, enhanced, or examined in detail while maintaining the overall anatomical context in the background, enabling both global overview and local detailed examination simultaneously.
Solution Approach 2:
The volume data is segmented into different anatomical structures using classification algorithms. Each structure is assigned a unique class label, allowing independent manipulation while maintaining the original volumetric data integrity. This enables selective movement and examination of individual structures without requiring separate surface models.
4Shape
If volume rendering is used to create three-dimensional representations, then anatomical structures are illustrated, but the data cannot be easily broken down into individual structures
Solution Approach 1:
The volume data is segmented into different anatomical structures using classification algorithms. Each structure is assigned a unique class label, allowing independent manipulation while maintaining the original volumetric data integrity. This enables selective movement and examination of individual structures without requiring separate surface models.
Solution Approach 2:
The system provides dynamic control over the visualization, allowing users to selectively display, hide, or manipulate individual classified structures within the volume rendering. The classification framework enables flexible reconfiguration of the displayed anatomy without regenerating the entire volume model.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for visualizing medical image data (BD) as volume data (VD) is described. In this method, medical image data (BD) is acquired. A 3D mask is generated by segmenting the image data (BD) and dividing the segmented areas (SG) into predefined classes. The image data (BD) and the mask data (MD) are then stored in two separate 3D texture files. A translation vector (Vec_tr) is then calculated, which describes the displacement of a segmented volume element (SG) between an origin position (pos_HF) and a target position (pos_HC). Furthermore, a visual representation of the image data (BD) is generated by applying a beam scanning method to the stored image data (BD). Finally, a displacement of a segmented volume element (SG) in the visual representation is performed by the translation vector (Vec_tr). A visualization device (50) is also described.Furthermore, a medical imaging system (60) is described.