1) The document discusses computational assessment methods for chronic obstructive pulmonary disease (COPD) using medical imaging data. It covers topics like lung segmentation, lobe segmentation, airway measurement, vessel quantification, and texture-based emphysema quantification. 2) Various algorithms are presented for tasks like robust lung and lobe segmentation, left/right lung splitting, airway skeletonization and labeling, and classification of pulmonary arteries and veins. 3) Quantitative image analysis methods are discussed for measuring airway wall thickness, quantifying emphysema heterogeneity, and classifying COPD patterns based on texture and shape features.

1. BASICS OF COMPUTATIONAL
ASSESSMENT FOR COPD
1st PERFUSE Registry Workshop
Namkug Kim, PhD
Medical Imaging & Robotics Lab.
University of Ulsan College of Medicine
Asan Medical Center
South Korea

2. Researches with
Hyundai Heavy Industries Co. Ltd.
LG Electronics
Coreline Soft Inc.
Osstem Implant
CGBio
VUNO
Kakaobrain
Conflict of Interests
Stockholder
Coreline Soft, Inc.
AnyMedi
Co-Founder
Somansa Inc.
Cybermed Inc.
Clinical Imaging Solution, Inc
AnyMedi, Inc.
Selected Grants as PI
National Research Foundation (한국연구재단),
South Korea
7T용 4D 자기공명유속영상을이용한 심뇌혈관 질환의 in-vivo
유동 정량화 SW개발, 2016
4D flow MRI을 이용한 심혈관 질환의 in-vivo유동 연구, 2015-7
자기공명분광영상 및 MRI의 통합 분석 소프트웨어 개발
KEIT (산업부), South Korea
의료영상 인공지능 과제, 2016-20
3DP 척추 맞춤형 임플란트, 2016-20
3D 프린터 기반 무치악 및 두개악안면결손환자용 수복
보철물 제작, 재건 시스템 개발, 2015-9
근골격계 복구 수술 로봇 개발, 2012-7
영상중재시술 로봇시스템 개발, 2012-7
Spine및Neurosurgery 수술보조용항법 시스템 개발, 2001
의료용 3차원 모델 제작 S/W 기술 개발, 정통부, 2000
의료영상재구성에 의한 가상시술 소프트웨어 개발,
중소기업기술혁신개발,중기청, 2001
KHIDI (보건복지부), South Korea
영상 뇌졸중 예후 예측 및 치료방침 결정 시스템 개발, 2012-8
관동맥 관류 CT 의 자동 진단 프로그램을활용한 허혈성
질환의 진단과 치료, 2013-6
RP를 이용한 척추나사못 삽입술 계획 프로그램 개발, 2000
Commercial collaboration
Hyundai Heavy Industry,Osstem Implant,S&G Biotech, Coreline
soft, MidasIT, AnyMedi,Hitachi Medical, Japan,

3. Overview of Lung Analysis*
512x512x(512~3000) voxels
Anatomical imaging,
Qualitative Diagnosis
Functional Imaging
MR/CT perfusion/ventilation
Classification
On texture , shape, etc
CADD (Computer Aided
Differential Diagnosis)
On diseases distribution, heterogeneity
Surgery Support
(replica model, surgery planning, Robot)
CBIR (Content based
Image Retrieval), CAD
Image
Repositories
Image Queries
Image Retrieval
Virtual bronchoscopy
Quantification
On airway measurement, EI, etc
Segmentation / Registration
Lung/Lobe/Airway Seg.
Full ins/ex lung registration
*Seo JB, Lee SM @ Radiology AMC (2004.8~)
On image based DB query and retrieval

4. Basic Lung Segmentation
Lung
Thoracic
Trunk
Rolling Ball AlgorithmRegion Growing AlgorithmAirway segmentation and left and right lung split
Overall procedure of lung and airway
segmentation Fast Seed Based Region Growing Rolling Ball Algorithm
Valid Region?
Input Seed Points
Save Seed Points
Retrieve a Seed Point
Region segmentation
using region growing
Save Seed Point
Queue Empty
Segmentation Result

5. Hyper-inflated Lung Left/Right Split
Lee MH, Kim N, et al, KSIIM 2011, Best Poster Award, J Digit Imag 2014
Surface fitting with iterative 3D morphological operator with Hessian
matrix analysis
For automatic
lobe
segmentation
Robust
left/right split
algorithm for
preserving lung
volume with
same threshold

6. Thoracic Cavity Segmentation
COPD is systematic
diseases
Quantification of
fat contents in the
thoracic cavity.
For robust
segmentation
Surface-fitting
method with 5
surfaces including
inner rib
boundary and
diaphragm
A two-stage level
set method using
a shape prior.
Additional
heart and its
surrounding
tissue
Volumetric overlap
ratio (VOR)
98.17 ± 0.84%,
6/33Bae JP, Kim N, et al, Med Physics, 2014
Heart and its surrounding tissues Segmentation Results
Surface fitting results

7. Robust Lobe Segmentation
Park JH, Kim N, et al, RSNA 2011, IWPFI 2017
Robust lobe
segmentation is not
easy
Anatomic & diseases
variations including
fake, incomplete
fissures, COPD and
ILD
For robust
segmentation
Surface-fitting
method with Hessian
matrix and machine
learning
Comparison among
machine learnings

8. Diaphragm Segmentation & Quantification
Chang YJ, Kim N , IWPFI 2013, Med Phys 2016

9. Robust Airway Measurement
Inflammation -> Airway Remodeling -> Airway
Narrowing
quantify the extent of airway remodeling in vivo
using CT
Typical surrogate marker
MDCT
Provides bronchial tree geometry with sub-
millimeter resolution
Measure the airway wall thickness, luminal diameter,
wall area, lumen area, wall-lumen area ratio and
wall-lumen diameter ratio
Evaluate the regional airway physiology and
structure
– For the development of disease affecting the airways,
such as asthma and chronic obstructive pulmonary
disease (COPD),
Quantitative Imaging Biomarker
For the determination of bronchial tree dimensions
to assess the efficacy of new drug trial [1,2]
1. Weibel ER, et al. Design and structure of human lung. In: Pulmonary disease and disorders. New York: McGraw-Hill, 1988:11-60
2. Barnes PJ, et al. Lancet 2004;364:985-996
Airway Wall Measurement
With FHWM*

10. Airway Segmentation /
Skeletonization / Labelling
Airway Tree
Segmentation
Lung Segmentation/Split
Input:
Output:
512x512x(300~2000) voxels
Anatomical imaging,
Qualitative Diagnosis
Skeletonization Labelling
Lung 2008, KJ Radiol 2008, KJ Radiol 2008

11. Airway Phantom Measurement
FWHM method
B: the physical phantom
filled with poly-urethan form
A: eleven artificial tubes of the
physical phantom without filled
poly-urethane foam
C: axial slice of phantom at no tilt.
D: axial slice of phantom tilted at
45’ to the scan plane.
* Kim N, Seo JB et al, Part I, II, Korean J Radiol 2008
Number of
tube
Inner Radius
Mean ± SD
Outer Radius
Mean ± SD
Wall thickness
Mean ± SD
1 0.66 1.56 ± 0.01 0.90 ± 0.01
2 0.63 1.08 ± 0.10 0.45 ± 0.10
3 2.13 5.21 ± 0.02 3.08 ± 0.02
4 1.66 4.12 ± 0.01 2.46 ± 0.01
5 1.8 3.01 ± 0.01 1.21 ± 0.01
6 1.63 2.59 ± 0.00 0.96 ± 0.00
7 1.5 2.06 ± 0.12 0.56 ± 0.12
8 3.23 6.04 ± 0.02 2.81 ± 0.02
9 2.34 4.07 ± 0.01 1.73 ± 0.01
10 4.23 6.01 ± 0.01 1.78 ± 0.01
11 3.51 5.09 ± 0.02 1.58 ± 0.02
Table 1. physical dimensions of artificial airways
Overall flow of airway measurement
Phantom Study Airway measurement
(Green – lumen,
Blue – normal wall
Cyan – mean of nl wall
Pink – outside of 2SD of nl wall
Red – mean of nl wall
Full Width at Half Maximum*
Lung 2008, KJ Radiol 2008, KJ Radiol 2008

12. Airway Phantom Measurement : Band
Integral Method
B: the physical phantom
filled with poly-urethan form
A: eleven artificial tubes of the
physical phantom without filled
poly-urethane foam
C: axial slice of phantom at no tilt.
D: axial slice of phantom tilted at
45’ to the scan plane.
Number of
tube
Inner Radius
Mean ± SD
Outer Radius
Mean ± SD
Wall thickness
Mean ± SD
1 0.66 1.56 ± 0.01 0.90 ± 0.01
2 0.63 1.08 ± 0.10 0.45 ± 0.10
3 2.13 5.21 ± 0.02 3.08 ± 0.02
4 1.66 4.12 ± 0.01 2.46 ± 0.01
5 1.8 3.01 ± 0.01 1.21 ± 0.01
6 1.63 2.59 ± 0.00 0.96 ± 0.00
7 1.5 2.06 ± 0.12 0.56 ± 0.12
8 3.23 6.04 ± 0.02 2.81 ± 0.02
9 2.34 4.07 ± 0.01 1.73 ± 0.01
10 4.23 6.01 ± 0.01 1.78 ± 0.01
11 3.51 5.09 ± 0.02 1.58 ± 0.02
Table 1. physical dimensions of artificial airways
Overall flow of airway measurement
Phantom Study
Band based avg Density Profile
J Comput Assist Tomogr. 2015

13. Classification of Pulmonary Artery and Vein
For COPD and
Pulmonary HT
Subtree extraction
Weighted minimal
spanning tree
by cutting branches
with lower labels
Park SY, Kim N, Seo JB, et al, MIRL, AMCMed Phys. 2013

14. Vessel Quantification
14/33
• 10 control with non-contrast volumetric chest CT scans
• The radius error :1.57±0.51 mm
• The direction error : 8.77±17.20%.
Bae JP, Kim N, et al, RSNA 2014

15. COPD Quantification S/W
Quantification S/W of Emphysema index on
HRCT
LAA (Low-Attenuation Area), Emphysema Index:
Area (volume) % below threshold (-950HU), Mean
Lung Density, Lung Volume
Lee YK, Kim N, Seo JB, et al, Lung 2008

16. Size based Emphysema Analysis
Flow
Hwang JE, Kim N, et al, IJ COPD 2016, Under Review : IJ COPD 2017

17. Quantitative Assessment of Regional
Heterogeneity of Emphysema
Functional silence of
upper lung
Automatic
quantification of
heterogeneity
Central to Peripheral
Anterior to Posterior
Upper to lower
Correlation with PFT FEV1 = 24.9 FEV1 = 22.5
• The severity of emphysema in lower lung affects
values of PFT more significantly than the severity of
emphysema in upper lung.
EJ Choi, N Kim, JB Seo, et al, AJ Radiol 2010

18. What is Texture?
Texton :
fundamental
element
Texture :
statistical
distribution of
texton
* P. Brodatz: Textures, A photographic album for artists and designers, Dover Publications, New York, 1966.
Examples *
Texton
Statistical
distribution

19. Classification of COPD Parenchyma
PLE or severe CLE Mild CLE
Bronchiolitis obliterans (BO) Normal
*Lee YJ, Kim N, Seo JB et al, provisionally accepted at CMPBInv Radiol 2008, Kim N, Seo JB, et al, J Digit Imag 2009

20. Shape Features
Cluster analysis
Preprocessing: segmentation (threshold:
- 960HU) and filtering
Cluster features
Number of Cluster
Size (Mean, SD)
Circularity (Mean, SD)
Aspect ratio: LR/SR (Mean, SD)
 Top-hat Transformation
 Extract contrasted component
according to the size or shape
 Suppress the effect of breathhold
variation
 Features from Top-hat
 White top-hat: mean, SD
 Black top-hat: mead, SD
Original Black Top-hat White Top-hat
Comput Meth Prog Bio 2009

21. Sensitivity/Specificity & Improvement
* Statistically significant difference (p<0.05)
Texture Shape
Texture+S
hape
Normal 92.6 72.5 93.8
BO 76.9 68.1 83.9
Mild CLE 78.5 82.2 92.8
PLE/severe CLE 95.9 87.3 99
Overall 85.8 77.2 92.2
0
10
20
30
40
50
60
70
80
90
100
Normal BO Mild CLE PLE/severe
CLE
Overall
Class
Sensitivity(%)
Texture
Shape
Texture+Shape
0
10
20
30
40
50
60
70
80
90
100
Normal BO Mild CLE PLE/severe
CLE
Class
Specificity(%)
Texture
Shape
Texture+Shape
Texture Shape
Texture+S
hape
Normal 96 90.3 97.3
BO 96.5 90.4 97.4
Mild CLE 91.6 93.2 96.5
PLE/severe CLE 98 96.1 98.6
0
2
4
6
8
10
12
14
16
Normal BO Mild CLE PLE/severe CLE
Improvementofsensitivity(%)
Improvement of
sensitivity (%) after
adding shape features
Normal 1.2
BO 7
Mild CLE 14.3
PLE/severe CLE 3.1
Kim N, Seo JB, et al, JDI 2011 2nd best paperComput Meth Prog Bio 2009

22. Texture-based Quantification
TEI : texture-based emphysema
index of HRCT
= 0.3 x ME% + SE%
DEI : density-based
emphysema index
DEIVol of volumetric CT
DEIHR of HRCT
SE : Severe Emphysema
ME : Mild Emphysema
BO : Bronchiolitis Obliterans
NL : Normal Lung
Park YS, Kim N, Seo JB, et al, Inv Radiol 2008

23. Texture-based Quantification
24
Mean area fraction of texture-based quantification
• Severe Emphysema : 12.5 ± 16.3 %
• Mild Emphysema : 24.0 ± 10.1 %
• Bronchiolitis Obliterans : 16.0 ± 10.1 %
• Normal Lung : 47.4 ± 26.3 %
Severe Emphysema
(SE)
Mild Emphysema
(ME)
Bronchiolitis
Obliterans (BO)
Normal
Lung (NL)
** Texture-based Quantification Image Color
SE ME BO NL
Park YS, Kim N, Seo JB, et al, IR 2008Park YS, Kim N, Seo JB, et al, Inv Radiol 2008

24. Texture analysis over cross-vendors
Study design Training set Test set Bayesian SVM p-value
GE GE GE 82.35 ± 2.82 92.34 ± 2.26 <0.001
Siemens Siemens Siemens 86.26 ± 3.16 91.53 ± 2.07 <0.001
IntegratedSet GE+Siemens GE+Siemens 76.65 ± 2.57 91.18 ± 1.91 <0.001
GE->Siemens GE Siemens 71.98 ± 2.94 82.33 ± 5.75 <0.001
Siemens->GE Siemens GE 71.71 ± 3.6 79.07 ± 3.27 <0.001
Park YJ, Kim N, Med Physics 2013
6 classes (a) Normal lung parenchyma,
(b) ground-glass opacity, (c)
consolidation, (d) reticular opacity, (e)
emphysema, and (f) honeycombing.
(normal; green, ground-glass opacity,
yellow; reticular opacity, cyan,
honeycombing, blue; emphysema, red;
and consolidation, pink). (a) GE CT
images (b) GE training on GE CT images,
(c) the Siemens training on GE CT images,
(d) integrated training data on GE CT
images.
Flow chart

25. Fissure Integrity
Twenty patients with severe COPD
for endobronchial valve volume reduction
procedure
Fissure Integrity Evaluation Process
Lung left and right split
lobe segmentation
Histogram analysis with maximum
likelihood threshold method.
Gold standards
Two thoracic radiologists (rad1, rad2)
Results
Completeness (CAD) : 0.982
Accuracy between computer and
radiologists : 85%
Cohen’s kappa values :
rad1 vs rad2, 0.694 / CAD vs rad1, 0.681 /
CAD vs rad2, 0.588 / CAD vs radc, 0.700).
26/33Lee MH, Kim N, et al, IWPFI 2017
Read CT data
Airway
segmentation
Lung
segmentation
Pulmonary
vessel
segmentation
Lobe
segmentation
Fissure
detection
Complete
fissure
Find FIR
Maximum
density
projection
Thresholding
Incomplete
fissure
Subtraction
Segmentation Fissure detection
Find FIR
Visual Scoring
by radiologists
Validation
Find
Maximum
density
value at z-
axis line
3D fissure
surface
Binary fissure
mask

26. Lung Registration for Air Trapping
Full inspiration CT
Registered full expiration CT
Full
Expiration CT
+
Inspiration + Expiration Subtraction color map
Deformation map**
(with respect to COI)
B-spline+
levelset
registration
Non-rigid Registration Result
**Color means deformable distance ( Near : R-G-B : Far)
Initial Rigid
Registration

27. Air Trapping by Using Automatic Registration in
COPD
Inspiration Expiration Registration
Lee HJ,Seo JB, unpublished dataLung 2008, KJ Radiol 2015, Eur Radiol 2016, Eur Radiol 2016

28. DECT Xenon Ventilation :AMC
First clinical study in the world
Research contract with Siemens Medical, CT (advisory board)
Pilot study
Normal volunteers
Patients: COPD, BE, BO
Protocol
DECT
30% Xenon inhalation
Xe ventilator, monitoring devices
Dynamic and static whole lung scan
Detect in vivo inert Xenon gas
Radiology 2008, Radiology 2010, Inv Radiol 2010, Inv Radiol 2016, Eur Radiol 2016
CT images K map in wash-in
K map in wash-out AUC map
in wash-in
 Kinetics of Xe
ventilation according
to Kety’s model
Ct = A (1 – e-Kt)

29. V/Q Mismatch)
Iodine Perfusion(Q) mapXenon Ventilation(V)
map
V/Q mismatch map
unpublishedRSNA 2014

30. Quantitative Imaging : Phantom calibration
waterarc
Outer air1
Inner air
Bed 1 Bed 2
Outer air2
CT Phantom
Manufact
urer
Scanner kVp Tube cur
rent
(mA)
Average e
ffective tu
be current
(mA)
Slice thick
ness
Pitch Gantry r
otation ti
me
Reconstruc
tion Filter
Siemens Sensation 16 140 200 100 0.7 1.000 0.5 B30
Siemens Sensation 64 140 270 99.9 0.7 1.000 0.37 B30
GE LightSpeed 16 140 190 101.2 0.625 0.938 0.5 Standard
GE LightSpeed VCT
64
140 250 101.6 0.625 0.984 0.4 Standard
Philips Brilliance 16 140 142 100.2 0.8 1.063 0.75 B
Philips Brilliance 64 140 135 99.9 0.625 1.014 0.75 B
Variation in Emphysema indexes (%) from
four different CT scanners.
Before DC After DC
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5
Time Point(s)
EmphysemaIndex(%)
Density Correction (outair) ; Standard ; -950 HU Thresholding
Siemens 16
Philips 16 (2)
Philips 40
Toshiba 64
Outsider air volume density correction
based on water and air in four different CT
scanners (Water was assumed as 0 HU and
air as -1000 HU).
FEV1 FEV1/FVC
Emphysema index
(Base)
-0.318
0.002
-0.510
<0.001
Emphysema index
(Inner air correction)
-0.597
<0.001
-0.612
<0.001
Emphysema index
(Outer air correction)
-0.394
<0.001
-0.497
<0.001
Mean lung density
(Base)
0.259
0.011
0.460
<0.001
Mean lung density
(Inner air correction)
0.487
<0.001
0.528
<0.001
Mean lung density
(Outer air correction)
0.383
<0.001
0.499
<0.001. Partial correlation analysis adjusted by age
and sex between CT and PFT parameters in
Philips and Toshiba (n=98).

31. Quantification of EI
DECT Xe Ventilation DECT Perfusion
MR PFT
O2 Ventilation
DCE-MRI perfusion
Wall thickness
Lung Evaluation Metrics
Lobe Segmentation
VQ Mismatch
Map
Size based EI
Disease Classification using Texture
Vessel analysis
Airway analysis
MRICT
Diaphragm
Thoracic Cavity
32
*Radiology, Med Phys 2013, Eur Radiol, JDI, Med Phys 2016, …

32. COPD Characterization
33
Perfusion
MR
Perfusion
Ventilation Emphysema
Structure
Micro
Structure
Machine Learning
Deep Learning
Air flowHemodynamics
Hwang HJ, Investigative Radiology (2016), E Beek, Clinics in chest medicine (2015), J thoracic imaging 29(2):80-
91, Hwang JE, IJ COPD (2016), J Applied Physiology (2007)
DECT
Perfusion
MR
Ventilation
DECT
Ventilation
VQ mismatch
VQ
3He MRI Diffusion
For Emphysema hol
Segmentation / (B0, B1 Correction) / Registration
Single Voxel
: Multi-
dimensional
Data
DWI/DTI
Perfusion
Ventilation
Texture Analysis
Modeling
Structure
VQ mismatch
Diffusion

33. Semi-Automatic Lobe Segmentation
 Efficient semi-automatic segmentation even in severe COPD patients
Whole Working Time < 5 min

34. Size based Emphysema Index
 Efficient semi-automatic segmentation even in severe COPD patients
Whole Working Time < 5 min

35. Ins/Ex. comparison

36. Airway Lumen/Wall Analysis
 Analyze whole airway tree automatically.

37. Deep Learning : Semantic Segmentation
Detection : RCNN, Fast RCNN, Faster RCNN, YOLO, …
Segmentation : FCN, Deconvnet, DCN with CRF, …
Super-resolution : SRCNN, DRCN

38. MR Segmentation
Supine (dimension reduction : 5 times)
About 160 slices
3Hr -> 10 sec

39. 2.5D CNN Airway Segmentation
80 COPD Patients’ Inspiration CT
69 CT volumes are included in training
11 CT volumes are NOT included in training
GS : Manual segmentation
41
Axial 3 slices, Sagittal 3 slices, Coronal 3 slices
32 x 32 x 3 x 3
Weights are shared
2-class
classification
CT
Volume
Probabilit
y
Volume
CNN
For each voxels inside lungs
Segment
ed
Airway
Hard thresholding (0.51) and
Select the connected component

40. CNN Airway Segmentation
Initial Manual
1~2Hr -> 2 min

41. Collaborators
• MIRL
• Clinical Collaborators@Asan Medical Center, SNU BH
– Radiology : Chest, Cardiac, Abdomen, Neuro/Brain
– Neurology :Dongwha Kang, Chongsik Lee, Jaehong Lee, Sangbeom Jun, Misun Kwon, Beomjun Kim
– Cardiology ; Jaekwan Song, Jongmin Song, Younghak Kim
– Internal Medicine : Jeongsik Byeon
– Pathology : Hyunhee Go
– Surgery : Bumsuk Go, JongHun Jeong, Songchuk Kim