Region of Interest Queries in CT Scans Matthias Schubert 1 Joint work with

INSTITUTE FOR INFORMATICS

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Region of Interest Queries in CT ScansMatthias 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


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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


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Head

=>

Computer Tomography:CT Scans

23000CWZ8S.0001145710.4.11, 16, 31, 46, 61, 76, 91, 106, 121

x

yz

Height

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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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


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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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Region of interest Query

target scan vi in PACS

ID of CT scan vi

Clie

ntSe

rver

Clie

nt

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)


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Outline





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Localization via Similarity Search

Example scan vq + chosen ROI



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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ROI Query Based on a Generalized Height Scale

gen. height scale H

CT scan vi in PACS

ID of CT scan vi

Clie

ntSe

rver

Clie

nt

CT scan vq

result

lbh ubh

lbi,s ubi,s

User-defined 3D

ROIlbˆq,s ubˆq,sInstance-based

Regression

Height axis (H scan)




Locate ROI

Trans-fer ROI

H

H

Iterative Interpolation


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Instance-based Regression: scan → H



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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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


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Outline





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Quality of Height Regression (scan → H)

020

040

060

080

010

000

2

4

0

200

400

# Scans (≈ „Database size / 450“)

Erro

r [c

m]

Tim

e / Q

uery

[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 5001234

0

1000

2000

3000

Feature DimensionEr

ror

[cm

]

Tim

e / Q

uery

[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


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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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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

runtimeretrieved slices

Runt

ime

[sec

]0

5010

1520

0 %

5 %

10 %

15 %

20 %

Retri

eved

frac

tion

of

com

plet

e vo

lum

es


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Outline





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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


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Thank you.


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Backup: Quality of Height Regression (scan → H)

0 200 400 600 80010000

1

2

3

4

0

100

200

300

400


Erro

r [c

m]

Tim

e / Q

uery

[ms]

Increased Quality and Runtime with Database size

Main Memory Runtimes

0 200 400 600 800 10000

1

2

3

4

02468101214


Erro

r [c

m]

Tim

e / Q

uery

[ms]

RCA dimension reduction + X-Tree Runtimes


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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]

0100200300400500

0510152025

Runtime [sec]

For 20 CT scans of 12,000 slices: total retrieval time = 1,400 seconds=> 70 to 99 % reduction of the retrieved volumes

Region of Interest Queries in CT Scans Matthias Schubert 1 Joint work with

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