1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Validation of Bone Models Using 3D Surface Scanning Nicole M. Grosland Vincent A. Magnotta

2. National Alliance for Medical Image Computing http://na-mic.org Validation True “gold-standard” often very difficult to achieve –Brain imaging often have to live with manual raters –Established guidelines based on anatomical experts Are there better “gold-standards” for other regions of the body?

3. National Alliance for Medical Image Computing http://na-mic.org Orthopaedic Imaging Ideas developed out of goal to automate the definition of regions of interest of the upper extremity –How can we validate these automated tools? Orthopaedic applications it is possible to dissect the region out of cadeveric specimens –Use specimen itself as the “gold-standard”

4. National Alliance for Medical Image Computing http://na-mic.org 3D Laser Scanner 3D Laser scanners have been used for rapid prototyping and to non- destructively image ancient artifacts Roland LPX-250 Scanner Obtained –Planar and rotary scanning modes –0.008 inch resolution in planar mode –Objects up to 10 inches wide and 16 inches tall can be scanned –Reverse modeling software tools

5. National Alliance for Medical Image Computing http://na-mic.org LPX-250 Laser Scanner

6. National Alliance for Medical Image Computing http://na-mic.org Specimens 15 cadaveric specimens were obtained spanning the distal radius to the finger tips –Specimens mounted on a Plexiglas sheet in the neutral position CT images collected on a Siemens Sensation 64 scanner –Images obtained with a 0.2x0.2x0.4mm resolution

7. National Alliance for Medical Image Computing http://na-mic.org Image Post Processing Resampled images to 0.2mm 3 resolution –Images cropped at the wrist Manually defined the proximal, medial, and distal phalanx bones –Two raters defined these regions on 11 fingers –Inter-rater reliability evaluated using relative overlap (0.91, 0.90, and 0.87 respectively) –Surfaces from the binary masks were generated

8. National Alliance for Medical Image Computing http://na-mic.org CT Scan

9. National Alliance for Medical Image Computing http://na-mic.org Finger Dissection Phalanx and metacarpal bones removed –Care taken to avoid tool marks on the bones De-fleshing process outlined by Donahue et al (2002) was utilized –Bones allowed to soak in a 5.25% sodium hypochlorite (bleach) solution for 6 hours Degreased via a soapy water solution Thin layer of white primer was used to coat the bony surfaces

10. National Alliance for Medical Image Computing http://na-mic.org Prepare Specimen for Scanning Deflesh, Degrease, Paint, Embed in Clay Scan Long Axis (Distal End Up) Using Dr. PICZA 4-Plane Scanning Scan Distal End Using Dr. PICZA 1-Plane Scanning Scan Long Axis (Proximal End Up) Using Dr. PICZA 4-Plane Scanning Scan Proximal End Using Dr. PICZA 1-Plane Scanning Edit the Scanned Surface Using Dr. PICZA Editing Tools Remove Noise, Delete Abnormal Faces, Create Polygon Mesh Use Pixform Software to Further Edit Surface Delete Extraneous Vertices, Fill Holes in Surface, Clean Non- manifold and Crossing Faces Align, Register, and Merge the Long Axis (Distal End Up) and the Distal End Align, Register, and Merge the Long Axis (Proximal End Up) and the Proximal End Align, Register, and Merge the Distal and Proximal Ends of the Bone Smooth Final Surface with a Tolerance of 0.10 mm

11. National Alliance for Medical Image Computing http://na-mic.org Proximal Bone Surface Scanning Steps Specimen CA05042125L Distal Up Distal EndProximal End Proximal Up Distal MergeProximal Merge Full Finger Scan

12. National Alliance for Medical Image Computing http://na-mic.org Proximal Bone – CA05042125L

13. National Alliance for Medical Image Computing http://na-mic.org Middle (Green) and Distal (Pink) Bones – CA05042125L

14. National Alliance for Medical Image Computing http://na-mic.org Full Finger – CA05042125L Full Finger – MD05010306R Full Finger Surface Scans

15. National Alliance for Medical Image Computing http://na-mic.org Full Finger – MD05042226L Full Finger – SC05030303R

16. National Alliance for Medical Image Computing http://na-mic.org Registration of Surfaces Surface scans origin shifted to center of mass and reoriented to have the same orientation as the CT data Surfaces registered using a rigid iterative closest point algorithm Compute Euclidean distance between the surfaces

17. National Alliance for Medical Image Computing http://na-mic.org Surface Distance Measurement Tool

18. National Alliance for Medical Image Computing http://na-mic.org Proximal Distance Map Laser scanned surface Traced surface Surface Distances: 1P-SC05030303R

19. National Alliance for Medical Image Computing http://na-mic.org Middle Distance Map Laser scanned surface Traced surface Surface Distances: 1M-SC05030303R

20. National Alliance for Medical Image Computing http://na-mic.org Distal Distance Map Laser scanned surface Traced surface Surface Distances: 1D-SC05030303R

21. National Alliance for Medical Image Computing http://na-mic.org Results Finger ID Average Distance between Surfaces 1P-SC05030303R0.145 1M-SC05030303R0.134 1D-SC05030303R0.142 1P-MD05010306R0.142 Average values0.142

22. National Alliance for Medical Image Computing http://na-mic.org Discussion Surface scans used to validate regions of interest generated via CT scans –Average distance less than 1 voxel (0.2mm) Surface scans can be used to evaluate image processing procedures –Validation of tracing guidelines –Amount of smoothing –Iso-surface threshold –Evaluation of automated segmentation routines

23. National Alliance for Medical Image Computing http://na-mic.org Future Work Make regional specific measurement –E.g. articulating surface Evaluate ANN segmentation using this technique Can this be used to evaluate soft tissue geometry?

24. National Alliance for Medical Image Computing http://na-mic.org Thanks Nicole Grosland – Project PI Esther Gassman –Manual Traces/Surface scanning Nicole Kallemeyn –Manual tracing and surface comparison Nicole Devries –Finger dissection and specimen prep Kiran Shivanna –Software development Stephanie Powell –Automated segmentation Work funded in part by NIH/NIBIB Grant 1R21EB001501-01A2