An AtlasAtlas-based Approach for
AutoAuto-Segmentation of Patient’
Patient’s
Anatomy
Lei Dong, Ph.D., and Yongbin Zhang, M.S.
Dept. of Radiation Physics
University of Texas M.D. Anderson Cancer
Center, Houston, Texas
Educational Objectives
Describe the concept of deformable image
registration methods for autoautosegmentation
Understand the achievable accuracy and
validation methods for anatomyanatomysegmentation
Illustrate applications of atlasatlas-based autoautosegmentation in radiation therapy
AAPM Annual Meeting
Automated Segmentation for
Radiotherapy Volume Definition
Anaheim, CA (July 29, 2009)
Components of Deformable
Image Registration
Deformable image registration
for auto-segmentation
Fixed
Image
Metric
Interpolator
Moving
Image
Transform
Optimizer
Auto-segmentation based on
deformable image registration
Deformable Transformation
Registration
Finding the correct geometrical transformation that
brings one image in precise spatial correspondence
with another image
y
y
Transform
x
Fixed Image
x
Moving Image
Deformable image registration is a voxel mapping process
Deformable image registration
is an ill-defined problem
There is no unique solution (degeneracy)
Similar voxels can be grouped differently
based on different rules.
There is only one truth
Usually we don’
don’t know the groundground-truth!
Definition: Segmentation
Resolving the boundaries of an object
Deformable image registration
for auto-segmentation
Step 1. Collect the reference CT image
and contours (atlas)
Step 2. Perform deformable image
registration between the reference CT and
the new CT
Step 3. Apply deformable transformation
to map the original contours on the
reference CT to the new CT
CT 1 (1/15/2003)
CT 2 (2/21/2003)
difference
Sparse (feature) based
Dense (image intensity) based
Example
H&N
CT-on-rails
Studies
Auto-Segmentation
(Intra-object)
Anatomy at two different time points for
the same patient
Deformable image
registration as a diffusion
process: the “demons”
algorithm.
Int. J. Radiat. Oncol. Biol.
Phys. 61 (3), 725-735 (2005).
There are many approaches for
deformable image registration
Dealing with relatively small deformations
Track changes
Baseline data are available and reliable
Wang et al.
Prostate Radiotherapy
CT to Cone-beam CT
Planning contours mapped to
24 in-room CTs
Planning CT
CBCT
Dotted line – rigid registration
Solid line – deformed registration
Wang H. et al. IJROBP 72 (1):210-219, 2008.
Auto-Segmentation of H&N
Anatomy for Adaptive RT
Head and Neck Case
upper
Planning contours were
automatically transformed to
subsequent 15 (daily) CT images
upper
lower
lower
Wang et al.
Deformable Image Registration For
ConeCone-beam CT
Planning CT
4D CT Contour Propagation
Treatment Day CBCT
Transverse
Sagittal
Coronal
Dong et al.
Joy Zhang et al.
Background
IMRT is becoming a major treatment
technique for radiation therapy
Inter-object Registration
AtlasAtlas-based autoauto-segmentation
Note: All IMRT patients treated at the main center.
Lei Dong 10/14/2008
InterInter-observer variation in contouring
IMRT requires
target and normal structure
contours
O'Daniel, Rosenthal et al. A J Clin Onco (2007).
Contouring from scratch vs. computer
assistant
Improvement in Consistency in
Contouring
(BOT)
(BOT)
Contouring from scratch
Contouring using deformed template
Chao KSC, et al. Int J Radiat Oncol Biol Phys 68 (5):1512-1521, 2007.
Contouring from scratch
Contouring using deformed template
Contouring from scratch vs.
computer assistant
Modified contours vs. unmodified
(deformed) contours
(NPX)
ROIs (cc)
VOI (Modified Contours vs.
Computed Generated Contours)
VOI
(min)
VOI
(max)
93%
88%
96%
1.0
0.3
95%
93%
97%
0.9
0.3
91%
87%
94%
0.9
0.6
89%
84%
96%
0.7
0.3
91%
86%
97%
0.6
0.4
93%
76%
100%
0.3
88%
77%
100%
1.0
0.6
82%
57%
96%
1.1
0.9
90.2%
81.1%
97.0%
0.8
0.5
CTV1
CTV2
CTV3
L parotid
R parotid
Spinal cord
Brainstem
Larynx
Average=
Volume Overlap Index (VOI):
VOI
VOI =
Contouring from scratch
Contouring using deformed template
BOT
Case
NPX
Case
Time to contour from
scratch (min)
24
43
38
65
50
45
69
60
49
Time to modify from
deformed contours (min)
29
24
31
16
37
29
20
35
28
Ratio
1.21
0.56
0.82
0.25
0.74
0.64
0.29
0.58
0.64
Time
Saving
(min)
-5
19
7
49
13
16
49
25
22
Physicians
1
2
3
4
5
6
7
8
Average
Time to contour from
Time to modify from
scratch (min)
deformed contours (min)
38
33
45
35
33
35
76
39
75
38
75
40
79
30
65
45
61
37
Ratio
0.87
0.78
1.06
0.51
0.51
0.53
0.38
0.69
0.67
Time
Saving
(min)
5
10
-2
37
37
35
49
20
24
0.3
3D Surface Distance
Vmodified I Vdeformed
(Vmodified + Vdeformed ) 2
(BOT Case)
Benefits for Experienced and
Inexperienced Physicians
Time Savings (minutes)
Physicians
1
2
3
4
5
6
7
8
Average
Distance
1SD (mm)
Agreement (mm)
Experienced
H&N IMRT
Physicians
1
3
6
7
Average
Time to contour from
Time to modify from
scratch (min)
deformed contours (min)
24
29
38
31
45
29
69
20
44
27
Inexperienced
H&N IMRT
Time to contour from
Physicians
scratch (min)
2
43
4
65
5
50
8
60
Average
55
BOT
Case
Time to modify from
deformed contours (min)
24
16
37
35
28
Ratio
1.21
0.82
0.64
0.29
0.74
Time
Saving
(min)
-5
7
16
49
17
Ratio
0.56
0.25
0.74
0.58
0.53
Time
Saving
(min)
19
49
13
25
27
Benefits for Experienced and
Inexperienced Physicians
Experienced
H&N IMRT
Physicians
1
3
6
7
Average
Time to modify from
deformed contours (min)
33
35
40
30
35
Ratio
0.87
1.06
0.53
0.38
0.71
Time
Saving
(min)
5
-2
35
49
22
Inexperienced
Time to modify from
H&N IMRT
Time to contour from
scratch (min)
deformed contours (min)
Physicians
2
45
35
4
76
39
5
75
38
8
65
45
Average
65
39
Ratio
0.78
0.51
0.51
0.69
0.62
Time
Saving
(min)
10
37
37
20
26
Time to contour from
scratch (min)
38
33
75
79
56
Application to automatic
whole breast segmentation
NPX
Case
Inter-observer Variations
Contour from Scratch
Contour from Template
V. Reed et al. IJROBP, 2009
Building a consensus breast
atlas using multiple physician’s
contours
Consensus Contours (green)
Statistical
Learning
V. Reed et al. IJROBP, 2009
Model Patient
Contour from Scratch
Contour from Template
New Patient
Template
Scratch
Units: cm
3D mean surface-to-surface
distance (mm)
Physician
number
Edited DEF- CTVwb from
SEG CTVwb
scratch
Time to finalize CTVwb
(min)
Edited DEFSEG CTVwb
Summary of Inter-object
deformable image registration
CTVwb
from scratch
1
1.0
1.0
18.0
16.9
2
1.5
2.7
8.4
8.9
3
0.4
1.1
15.6
24.3
4
0.6
1.3
10.1
18.4
5
0.8
1.5
6.6
18.7
6
1.4
1.3
25.4
23.2
7
0.7
2.9
3.4
10.3
8
1.3
1.2
35.9
45.2
Median
0.9
1.3
12.9
18.6
Average
1.0
1.6
15.4
20.7
Atlas based segmentation is promising
Challenge in building an appropriate atlas
Challenge in large differences among
different patients
Challenge in accuracy
Manual correction is still needed
Challenge in registration ambiguity
(30%
improvement)
Incorrectly Mapped Contours
Correspondence Ambiguity
No correspondence
Large deformation
Planning CT
planning
Daily CT
daily
?
How to handle foreign object?
Gao et al. Med Phys 33 (9) 2006.
39
40
Image Intensity Modification
Prior knowledge can be used
The gas will only be produced within the rectum,
Contours of rectum will be in the planning CT.
With Image Intensity Modification
planning
Rectal wall is more important for radiotherapy.
daily
42
Summary
Deformable image registration is an effective
method for intraintra-object registration – Adaptive
radiotherapy
Recent research demonstrates the usability of
tools in both clinical and research environment
DIR shows promise for interinter-object autoautosegmentation
There are still many challenges
Validation of the segmentation
Registration ambiguity
Robust and accurate algorithms for CBCT