institute for informatics database group region of interest queries in ct scans matthias schubert 1...
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INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans
Matthias Schubert1
Joint work with
Alexander Cavallaro2, Franz Graf1, Hans-Peter Kriegel1, Marisa Thoma1
1 Ludwig-Maximilians-Universität München, Database Group2 Imaging Science Institute, University Hospital Erlangen
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 2
Outline
• ROI Queries on CT-Scans
• ROI Retrieval Based On a General Height Scale:
• Simple Solution based on Similarity Search
• Solution based on Generalized Height Scale
• kNN Regression for mapping slices
• Iterative interpolation
• Experimental Validation
• Summary
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 3
Head
=>
Computer Tomography:CT Scans
23000CWZ8S.0001145710.4.11, 16, 31, 46, 61, 76, 91, 106, 121
x
yz
Heigh
t axis • 3-dimensional grid of 12 bit
grey values
• Depending on resolution: few MB to multiple GB (Example: 2.25 MB)
• Image strongly depends on the used scanner and the scan parameters
• DICOM header contains some meta information for each slice
• DICOM headers are mostly empty or even misleading
<=
Feet
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DATABASE GROUP
Region of Interest Queries in CT Scans 4
Picture Archieving and Communication Systems
• Scans are stored in Picture Archiving and Communication Systems(PACS)
• Scan retrieval by patient name, time, DICOM information
• Querying parts of scans is not supported very well=> load and transmit complete scan
• No access to sub volumes specified by an example
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 5
Problems
• slices outside the ROI increase the transfer volume
• bottleneck is the LAN:
large transfer times (up to minutes)
bandwidth in LAN is a limiting factor
• Only transferring the ROI requires tracking it
slice numbers in the target scan are not the same as in the query scan (scan regions vary)
positions might vary between scans and patients
(organ positions vary)
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DATABASE GROUP
Region of Interest Queries in CT Scans 6
Region of interest Query
target scan vi in PACS
ID of CT scan vi
Clien
tS
erv
er
Clien
t
CT scan vq
result
User-defined 3D
ROIExample scan vq + chosen ROI
Target scan vi on remote Server
Matching ROI in target scan vi
Locate ROI
Trans-fer ROI
Image database
(PACS)
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 7
Outline
• ROI Queries on CT-Scans
• ROI Retrieval Based On a General Height Scale:
• Simple Solution based on Similarity Search
• Solution based on Generalized Height Scale
• kNN Regression for mapping slices
• Iterative interpolation
• Experimental Validation
• Summary
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 8
Localization via Similarity Search
Example scan vq + chosen ROI
Target scan vi on remote Server
Matching ROI in target scan vi
Locate ROI
Trans-fer ROI
Short Commings:
• Requires pre-processing or heavy load on server:For each slice in target scan:
Feature Transformation
Comparison to ROI
• Feature similarity is influenced by global scan similarity:
• Scan parameters
• Patient characteristics
=> Direct similarity often fails
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DATABASE GROUP
Region of Interest Queries in CT Scans 9
ROI Query Based on a Generalized Height Scale
gen. height scale H
CT scan vi in PACS
ID of CT scan vi
Clien
tS
erv
er
Clien
t
CT scan vq
result
lbh ubh
lbi,s ubi,s
User-defined 3D
ROIlbˆq,s ubˆq,s
Instance-based Regression
Height axis (H scan)
Example scan vq + chosen ROI
Target scan vi on remote Server
Matching ROI in target scan vi
Locate ROI
Trans-fer ROI
H
H
Iterative Interpolation
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 10
Instance-based Regression: scan → H
Example scan vq + chosen ROI
Target scan vi on remote Server
Locate ROI
Better: Large training set
• Provides multiple examples annotated within consensus height space H
• More stable results
Training Database: 2D image features of height-annotated CT slices of multiple scans
H
H
H
k-NN query:
Consensus height h H
Speed-up Measures:
• Dimension reduction: RCA
• Spatial Indexing: X-Tree
Bar-Hillel et al: Learning distance functions
using equivalence relations, ICML‘03
Berchtold et al: The X-Tree: An index structure for highdimensional data,
VLDB‘96
Emrich et al: CT Slice Localization via Instance-Based Regression, SPIE‘10
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DATABASE GROUP
Region of Interest Queries in CT Scans 11
Iterative Interpolation H → scan
Combine Regression Mapping with Interpolation
height space H
CT scan vi (in PACS)
lbh ubh
lbi,s ubi,s
REG0,ih
REG1)(
ˆii,zh
1 1
Estimate location of vi in H via regression
1Interpolate target positions and . for hlb and hub
lbi,s ubi,s
2
2 2
3 3
Verify target positions via regression
3
Refinement Interpolation
Accept Result
vi
Hlbh ubh
0REG0,ih
REG1)(
ˆii,zh
lbi,s
ubi,s
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 12
Outline
• ROI Queries on CT-Scans
• ROI Retrieval Based On a General Height Scale:
• Simple Solution based on Similarity Search
• Solution based on Generalized Height Scale
• kNN Regression for mapping slices
• Iterative interpolation
• Experimental Validation
• Summary
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 13
Quality of Height Regression (scan → H)
020
040
060
080
010
000
2
4
0
200
400
# Scans (≈ „Database size / 450“)
Err
or
[cm
]
Tim
e /
Qu
ery
[m
s]
Quality and Runtime w.r.t. Training Database size
Main Memory Runtimes on original,175-dimensional Image Features
0 10 20 30 40 500
1
2
3
4
0
1000
2000
3000
Feature DimensionErr
or
[cm
]
Tim
e /
Qu
ery
[m
s]
On-disc runtime for dataset of 2103 CT scans (= 0.9 Mio slices) after RCA dimension reduction + X-Tree Indexing: dim 10 => 20 msWith feature generation and dimension reduction: Time / Query = 40 ms
Error= 1.98 cm
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 14
Validation of ROI Query Pipeline
Testing Height Range Queries on 5 manually-annotated Landmarks in 33 CT Scans:
lower bound of coccyx
lower plate of the 12th thoracic vertebra
sacral promontory lower xiphoid process
cranial sternum
• Annotation Error (LB + UB): 2.6 cm
• ROI Query Error (LB + UB): 2.6 – 2.4 cm
• ROI Query Runtimes: 1.3 – 10
secondsPays off if 8 slices are saved
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DATABASE GROUP
Region of Interest Queries in CT Scans 15
Runtime Advantages
Retrieval Times for Typical Queries:
Test on 20 CT scans of 12,000 slicesComplete Retrieval time: 70 s per scan
=> 70 to 99 % reduction of the retrieved volumes
Left kidney16.8 cm
Urinary bladder9.6 cm
Hip to lower L54.7 cm
Arch of aorta0.9 cm
runtime
retrieved slices
Runti
me [
sec]
050
10
15
20
0 %
5 %
10
%15
%20
%
Retr
ieved f
ract
ion o
f co
mple
te v
olu
mes
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 16
Outline
• ROI Queries on CT-Scans
• ROI Retrieval Based On a General Height Scale:
• Simple Solution based on Similarity Search
• Solution based on Generalized Height Scale
• kNN Regression for mapping slices
• Iterative interpolation
• Experimental Validation
• Summary
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 17
Conclusion and Outlook
Introduced ROI Query Framework:
• Great speed-up of CT subvolume retrieval queries
• Low costs and low error of localization
• Example-based queries are extensible to queries using anatomical atlases
Future Work:
• Extension of height queries to arbitrary 3D queries
• Test on alternative, non-medical use cases
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 19
Backup: Quality of Height Regression (scan → H)
0 200 400 600 80010000
1
2
3
4
0
100
200
300
400
# Scans (≈ „Database size / 450“)
Err
or
[cm
]
Tim
e /
Qu
ery
[m
s]
Increased Quality and Runtime with Database size
Main Memory Runtimes
0 200 400 600 800 10000
1
2
3
4
02468101214
# Scans (≈ „Database size / 450“)
Err
or
[cm
]
Tim
e /
Qu
ery
[m
s]
RCA dimension reduction + X-Tree Runtimes
INSTITUTE FOR INFORMATICS
DATABASE GROUP
Region of Interest Queries in CT Scans 20
Backup: Runtime Advantages
Simulated real-wolrd queries of varying heights:
Left kidney [16.8 cm]
Urinary bladder [9.6
cm]
Hip to lower L5 [4.7 cm]
Arch or aorta [0.9 cm]
0
100
200
300
400
500
0
5
10
15
20
25
Runtime [sec]
For 20 CT scans of 12,000 slices: total retrieval time = 1,400 seconds=> 70 to 99 % reduction of the retrieved volumes