Automated renderer for visible human and volumetric scan segmentations

Area of Science:

• Medical Imaging
• Computer Graphics
• Anatomical Visualization

Background:

• Accurate anatomical models are crucial for medical education and research.
• Current methods for generating 3D anatomical models can be time-consuming and complex.
• Standardized anatomical ontologies are needed for consistent data representation.

Purpose of the Study:

• To develop an automated and flexible method for generating 3D iso-surface models of abdominal anatomy.
• To create a library of binary segmentation mask sequences based on SNOMED CT.
• To enable flexible visualization of arbitrary organ groups for anatomical education.

Main Methods:

• Utilized binary segmentation mask sequences derived from Visible Human data.
• Developed an automatic method for generating iso-surface models from named structure masks.
• Integrated SNOMED CT hierarchy for anatomical structure labeling.
• Computed visualizations of selected organ groups.

Main Results:

• Successfully generated a library of abdominal anatomy segmentation masks.
• Established a reproducible and automated pipeline for 3D model creation.
• Demonstrated the ability to visualize arbitrary combinations of organs.
• Validated the utility of the generated models for anatomical teaching.

Conclusions:

• The developed method provides an efficient and flexible approach to anatomical model generation.
• This technique facilitates the creation of detailed 3D visualizations for medical training.
• The use of SNOMED CT ensures standardized anatomical representation.
• The generated models hold significant potential for virtual reality-based anatomy education.