Tooth Segmentation on Dental Meshes Using Morphologic Skeleton Kan WU School of Software Tsinghua...
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Tooth Segmentation on Dental Meshes Using Morphologic Skeleton Kan WU School of Software Tsinghua University, P. R. of China M.Eng. Li CHEN Ph.D. CAD/Graphics 2013, Hong Kong Jing LI Department of Orthodontics Peking University School and Hospital of Stomatology, P. R. of China Ph.D. Yanheng ZHOU Ph.D. Computers & Graphics
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Transcript of Tooth Segmentation on Dental Meshes Using Morphologic Skeleton Kan WU School of Software Tsinghua...
Tooth Segmentation on Dental Meshes
Using Morphologic Skeleton
Kan WUSchool of Software
Tsinghua University, P. R. of China
M.Eng. Li CHEN Ph.D.
CAD/Graphics 2013, Hong Kong
Jing LIDepartment of Orthodontics
Peking University School and Hospital of Stomatology, P. R. of China
Ph.D. Yanheng ZHOU Ph.D.
Computers & Graphics
Background
our work
Contribution
significantly reduced user interaction
avoid complex mesh feature estimation
an applicable pipeline for dental mesh segmentation
experiments on various clinical cases of different tooth
shapes and various levels of crowding problems
Problems of Current Work
intensive interaction
affected by feature disturbance
not sufficiently accurate Kumar et al.
2011
Kronfeld et al.
2010
“3Shape”
A Good Dental Segmentation Approach Should
locate teeth area automatically
separate adjacent teeth automatically
less dependent on complex feature estimation
smoothed & fitted boundary
morphologic
skeleton
Why Morphologic Skeleton
insensitive to feature missing & disturbance
simplified approximation of mesh features
easy separation of adjacent objects
ACCURACY
EFFICIENCY
REDUCED INTERACTION
Dental Mesh Segmentation Pipeline
1st Step: Locating Teeth Parts
automaticplane cutting
region-growing
skeletonization
original mesh
PCA-basedplane
initialization
energy field
1st Step: Locating Teeth Parts – (1)Estimating Cutting Plane
(1)Estimating Cutting Plane – PCA-based Plane Initialization
Kronfeld et al., 2010
set of feature vertices
barycentric point
eigenvector corresponding to
the smallest eigenvalue
weighted distance
feature pointsconnected to
v
(1)Estimating Cutting Plane – Energy Field
skeleton
1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization
curvature threshholdin
g
connectivity filtering
morphologicoperation
skeletonization
original morphologic skeleton(Rossl et al., 2000)
improved morphologic skeleton
1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization
seed points
skeleton
1st Step: Locating Teeth Parts – (3)Region-Growing
cut
2nd Step: Separating Teeth
valid cutdiscarded cut
2nd Step: Separating Teeth – Various Scenarios
2nd Step: Separating Teeth – Results
3D contours
2D contours
sampled 2D contours sampled 3D contours
interpolated 3D contours
3rd Step: Smoothing Tooth Contours
Direction Change Measure Length Change Measure
center point for contour
middle point
3rd Step: Smoothing Tooth Contours – 2D Sampling
Direction Change Measures
Length Measures
sign(x) = 1 if x > 0, otherwise -1
3rd Step: Smoothing Tooth Contours – 2D Sampling
Results – Mild Tooth Crowding
original model cutting planeestimation
skeletonization &
region-growing
separating &contour
smoothing
Results – Moderate Tooth Crowding
original model cutting planeestimation
skeletonization &
region-growing
separating &contour
smoothing
Results – Severe Tooth Crowding
original model cutting planeestimation
skeletonization &
region-growing
separating &contour
smoothing
Results
Results
Results
Kumar et al. 2011
Kronfeld et al. 2010
our approach
our approach
Comparative Results – Published Approaches
when user interaction is not sufficiently accurate enough
“3Shape” Software
“3Shape” Software
our approach
our approach
Comparative Results – “3Shape” Software
Accuracy Evaluation – Mean Errors
The mean errors that compare our results to manually labeled ground truth. The unit is mm.
the distribution of particular error values across all segmented boundary vertices. The blue, yellow, red lines indicate the ranges of [0, 0.25], [0.25, 0.5], [0.5, 1.5], respectively.
Accuracy Evaluation – Error Distribution
User Interaction Evaluation
Time consumed by user interactions. The blue and yellow lines indicate manual boundary completion and additional seed adding, respectively. The unit is s
Limitations
a dental mesh benchmark
GPU accelerating
completely eliminate user interaction
Future Work
user interaction still needed
no GPU accelerating
Demo
Kan WU ([email protected])
THANK YOU
Li CHEN ([email protected])
Jing LI ([email protected]) Yanheng ZHOU ([email protected])
