2017 Slicer Western Week/3D Anatomical Segmentation to Improve Gross Anatomy

From VASST Wiki

Key Investigators

Steven Lewis (University at Buffalo)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Automated 3D reconstruction of organ systems from donated cadaver CT scans to assist gross anatomy students in understand normal physiological variation and the relationship between form and function
  • Utilize VR technology to create 3D displays of organ segmentations before and during dissections to assist in preparation and planning, and to allow for better retention of anatomical information post coursework
  • Utilizes segmentation workflow to build an automated protocol for segmenting anatomical structures from CT data
  • Analyze anatomical structures for variation and physiologically relevant features
  • A challenge will be to understand adaptive segmentation methods with Slicer as variation requires such
  • In addition, the cadaver images were taken without contrast and thus soft tissue discrimination might force limitations on segmentation of certain anatomical structures or could require a broader range of adaptive segmentation techniques
  • Finally, the automation must be capable of high throughput as sampling will be performed on a yearly basis for gross anatomy labs
  • The workshop provided an opportunity to understand the 3D Slicer platform better, and how to gain support for the development process behind automated segmentation, and module/extension design.
  • With the assistance of the experts in attendance, the manual segmentation workflow was also able to be better understood.
  • The next steps are either to begin creating an atlas from manual segmentations in order to utilize the atlas based automated segmentation modules, or to program new automated segmentation modules/extensions.

Background and References

https://www.nlm.nih.gov/research/visible/visible_human.html