1. The document discusses applying 3D printing to medical imaging by discussing medical image modeling. 2. It covers topics like virtual surgery modeling, 3D printers, image scanners, requirements for 3D printed medical models, and the medical imaging workflow from acquisition to 3D printing and post-processing. 3. Key aspects of medical image-based 3D printing discussed are image segmentation, surface modeling, and an in-house software for virtual simulated surgery.

1. 의료 영역에서의 3D 프린팅 적용을
위한 의료영상 모델링
Namkug Kim, PhD
Medical Imaging & Robotics Lab. (MIRL)
Convergence Medicine/Radiology
University of Ulsan College of Medicine
Asan Medical Center
South Korea

2. 모델링 가상수술
3D 프린터
영상 스캐너
3D 스캐너
투명, 연성 강성
골친화도
생분해성
Radiographer
CAD/CAM engineer
3DP engineer
Cloud
Mobile
STL
Infrastructure Components
플랫폼
SW HW 재료
인력

3. 의료영상 기반 3D 프린팅
512x512x(512~3000) voxels
Anatomical imaging,
Qualitative Diagnosis
Segmentation
Lung/Lobe/Airway Seg.
Full ins/ex lung registration
Surface Modelling
Marching cubes, iso-contour extractor
BSSRO Virtual Simulated Surgery
In-house SW : AViews
CAD
Smoothing, Decimation, Thinning.
*MIRL@AMC
3D Printing & Post-processing
(Planner based 3D printing)

4. Always! Paradigm Shifts in Radiology
Qualitative Dx
Film Sharing
Quantitative Dx
Digital Sharing
(Network)
CAx
Tech
Technology Push
Being Digital
3D Technology
Computer Aided Tech.
Artificial Intelligent
Smart/Mobile
Model based…
Domain (Medical)
Need
Aging Society
Evidence based
Minimal invasive
Quantification
Fast Drug Development
Safety…
Rapid Development using
high tech solution
Greek
& Roman
Era
0 AD
1543 AD
Copernican
heliocentrism
1668AD
Newton
Opticks
1905AD
Einstein
relativity
1687AD
Newton
Principia
1930AD
Quantum
Mechanics
1st TV
Broadcast
1950AD
Plate
Tectonics
US
DNA
1789AD
Chemical
revolution
1859AD
Origin of
species
1915AD
Mendelian
inheritance
1946AD
Computer
ENIAC
1995AD
Being digital
Internet
2005AD
Ubiquitous
& Smart
1907AD
1st Radio
Broadcast
1970AD
CT, MRI
Nobel,PET
1895AD
1st Xray
1983AD
PACS
1920AD
AART
2000AD
CAD

5. History of Medical Imaging
1972 Hounsfield, CT, Nobel Prize in Medicine in 1979
1962 Kuhl, SPECT and PET
1968 Targeted contrast agents
1963 Wright, Meyerdirk, Ultrasound
1967
1977
The first clinical MRI
Lauterbur, Mansfield , MRI, Nobel Prize in Medicine in 2003
1944
1952
1991
2002
Rabi, Nobel Prize in Physics for his resonance method for recording the magnetic properties of atomic nuclei
Bloch, Purcell, Nobel Prize in Physics for nuclear magnetic precision measurements and discoveries in connection therewith
Enrst, Nobel Prize in Chemistry for the methodology of high resolution NMRS
Wüthrich, Nobel Prize in Chemistry for nMRS for determining the 3D structure of biological macromolecules in solution
2005 Smart, Mobile, Ubiquitous
1988 3D Computer Graphics
1986 Self expanding stent
2000 Computer Aided Diagnosis
1983 Picture Archiving & Communication System
1905 The first English book on Chest Radiography
1896 First clinical X-ray radiolgraph
1895 Nobel Prize in Physics 1901 for X-ray Discovery
1917
1915
1914 Von Laue, Nobel Prize in Physics for x-ray diffraction from crystals.
Bragg and Bragg, Nobel Prize in Physics for crystal structure derived from x-ray diffraction
Barkla. Nobel Prize in Physics for characteristic radiation of elements
1936
1927
1924 Siegbahn ,Nobel Prize in Physics for x-ray spectroscopy
Compton , Nobel Prize in Physics for scattering of x-rays by electrons
Debye , Nobel Prize in Chemistry for diffraction of x-rays and electrons in gases
1920 The first ASRT in Chicago

6. Challenges in Medicine(Radiology & Surgery)?
7
ClinicalNeeds
Evidencebased
Minimalinvasive
Quantification
FastDrugDevelopment
Safety…
TechnicalSupports
BeingDigital
3
DTechnology
ComputerAidedTech.
ArtificialIntelligent
Smart/Mobile
Modelbased…
Picture Archive &
Communication System Minimal Invasive
Surgery
Computer aided
Surgery
Computer Aided
Diagnosis (CAD)
Quantitative
Imaging Surgical Navigator Functional/
Molecular Imaging
Robotic
Simulator
Imaging
Biomarker
CBIR Single Port
Surgery
Artificial
implant
Imaging
genomics
7T MRI Full DNA
sequencing
Future
Medicine
Quantitative
surgery
Robot Surgery
Personalized
Medicine
NOTESQIBA
…
Future Medicine
Being digital, Computer aided X
Evidence based Medicine
Quantitative Medicine -> Imaging Biomarker, QIBA,Quantitative Surgery
etc
Molecular/Functional Imaging
Personalized Medicine -> DNA Revolution, Full DNA Seqeuncing
Minimal Invasive -> Single Port, NOTES, Robotic Interventions

7. Imaging Revolutions : CT
•Developed the first CT Scanner at
EMI
• With A.M. Cormack
• Received the Nobel Prize for
Medicine in 1979
CT scanning the early days ,E C BECKMANN, The British Journal of Radiology, 79
The original lathe bed model ©EMI Ltd
Pages from Dr Perry's notebooks on the
first EMI scanner
First Brain CT Scan at Atkinson Morley’s
Hospital on 1971-10-1
Mechanical drawing of the ACTA
scanner from Dr. Ledley's patent

9. Leonardo da Vinci (1487 - 1489)

10. 기본 원리
여러각도에서 투사된 방사선을 검출하여
인체 단면을 computer를 이용하여
재구성

11. 기존 X-Ray의 단점

13. 감약계수(Attentuation Values)
균질의 X선속이 동일한 흡수체를 통과할 때 이의 약해짐은 지수법칙을 따르는
계수와 관계 -> 이를 선감약계수라 함
x
m
No
x
io eNN 
 m
x
io eNN 
 )( 321 mmm
Ni
x
X-rays
Attenuated
more
NoNi
Ni: input intensity of X-ray
No: output intensity of X-ray
m: linear X-ray attenuation

14. CT Number
물의 감약계수에 대한 상대적인 값
인체 조직중 골을 1, 공기를 -1로 한 후
확대정수를 곱한값
CT Number = K
mw
- mwm K : 확대 정수
mw: 물의 감약계수
m : 측정된 조직의 감약계수
EMI scale K=500
Hounsfiled scale K=1000

15. Third & Fourth Generations
(From Picker)
(From Siemens)

18. Slip Ring
Power Block : X-Ray Tube
Signal Block : DAS(Data)
Control Block : Collimator
Rotating Time

19. • Clockwise(CW)
• Stop
• Table Move
(Interscan Delay Time)
• Countered
Clockwise(CCW)
Conventional Vs Single Spiral
Scan Time (3 - 10 Sec) 0.75 – 2 Sec
Interscan Delay Time (10 – 20 Sec) 0 Sec
Exam Time ( 10 - 30 Min) 20 – 80 Sec

23. True 0.5 mm
Ultrahigh Resolution 24 LP/CM

24. Ultra-Low Dose

25. Imaging Revolutions : MR
Signos Mobile US imager approved by FDA
PACS : Aquarius NET : Thin clientFirst Observation of the
Chemical Shift**First NMR Spectra on Water*
Nobel prizes
1944 Physics : "for his resonance method for recording the magnetic properties
of atomic nuclei" Rabi (Columbia)
1952 Physics : "for their development of new methods for nuclear magnetic
precision measurements and discoveries in connection therewith"
Bloch (Stanford), Purcell (Harvard)
1991 Chemistry : "for his contributions to the development of the methodology
of high resolution NMRS". Ernst (ETH)
2002 Chemistry : “for his development of nMRS for determining the 3D structure
of biological macromolecules in solution” Wüthrich (ETH)
2003 Medicine : “for their discoveries concerning MRI” Lauterbur (University of Illi
nois in Urbana ), Mansfield (University of Nottingham)
2009 Medicine : “for functional MRI”, Seiji Ogawa (Nominated)

26.  Technical Development
 Being digital
 3D or 12bit Displays
 Imaging Modality
 PACS, EMR, etc
 Quantitative Imaging,
Imaging Biomarker
MEGA-Trends in HealthCare
Being digital

27. CT, MR, US
Printing of Image on
film
Radiologist
Radiologist
Digital
mages
Printing of Image on
film
Referring
Physician
Workflow related to Images

28. PACS (Pictures Archive and Communication System)
What is PACS?
PACS is a system that manages the Archive, Utilization of Medical Images
acquired during the practice of medicine
Image DB
Requires massive amount of data storage
Infrastructure for the medical imaging study, including 3D visualization
Basic PACS Architecture
PACS DB
MRI
CT X_Ray
Diagnostics
Medical Education

29. D I C O M 3 . 0
PACS (Pictures Archive and Communication System)
DICOM 3.0
Standard for Digital Image Communication set by NEMA
Most machines support this file type
Applies to all image formats (X-Ray, CT, MRI, etc…)

30. * DICOM - Digital Imaging and Communication in Medicine의 약자로써 ACR-
NEMA (American College of Radiology - National
Electronic Manufacturers’ Association)에서 제정한
의료 영상 정보의 통신 및 처리 Protocol.
* ACR-NEMA 2.0 : 기존의 의료 영상 정보 통신 및 처리 Protocol, Point-to-Point
Communication
: American College of Radiologists National Electrical Manufacturers' Association
* DICOM 3.0은 아직도 그 개발이 진행중. Supplementary들이 계속 발행되고
있다.
* Parts of DICOM 3.0 : PS 3.1 - 1992 ~ PS 3.13 - 1995
cf) PS 3.X-YYYY : DICOM 3.0 part X, Published in YYYY
DICOM

31. DICOM (계속)
* DICOM 3.0
PS 3.1 : Introduction and Overview (1992)
PS 3.2 : Conformance (1993)
PS 3.3 : Information Object Definitions (1993)
PS 3.4 : Service Class Specifications (1993)
PS 3.5 : Data Structure and Semantics (1993)
PS 3.6 : Data Dictionary (1993)
PS 3.7 : Message Exchange (1993)
PS 3.8 : Network Communication Support for Message Exchange (1992)
PS 3.9 : Point to Point Communication Support for Message Exchange
(1993)
PS 3.10 : Media Storage and File Format for Media Exchange (1995)
PS 3.11 : Media Storage Application Profiles (1995)
PS 3.12 : Media Formats and Physical Media for Media Interchange (1995)
PS 3.13 : Print Management Point to Point Communication Support (1995)

32. Communication
* DICOM 3.0 supports both point-to-point communication and Network
Communication
* Network Communication - ISO-OSI, TCP/IP
* DICOM에서의 Network protocol은 새로운 Communication protocol이 아니라
기존의 Protocol stack model을 이용하도록 설계되어 있다.
USER
Physical Layer
Stacks
DICOM 3.0

33. Structure of a DICOM 3.0 File
File
Preamble
DICOM
Prefix
Other
Mandatory elem.
Data SetData Set
Data Element Data ElementData ElementData Element
Tags
(4 Bytes)
Value
Field
(VL Bytes)
Value
Length
(2 Bytes)
Value
Representation
(2 Bytes)
파일 시작
Tags
(4 Bytes)
Value
Field
(VL Bytes)
Value
Length
(2 Bytes)
Value
Representation
(4 Bytes)
Tags
(4 Bytes)
Value
Field
(VL Byte or
Undefined Length)
32 bit UI
Value
Length
(4 Bytes)
Data Element Structure
For Explicit VR
Data Element Structure
For Explicit VR of OB, OW, SQ
Data Element Structure
For Implicit VR
DICOM 3.0 Meta header element
...

34. Basic DICOM Protocols
* DICOM 3.0의 base protocol
Endian : 2 Byte (16 bit) number를 주고 받는데 있어서의 Ordering 방법
> Little Endian : Least significant byte is sent (stored) first.
Ex. (0002,0002) -> 02 00 02 00
> Big Endian : Most significant byte is sent (stored) last.
Ex. (0002,0002) -> 00 02 00 02

35. 5. Column (0028,0011)
이미지에 있는 pixel들의 Column의 수.
6. Bits Allocated (0028,0100)
하나의 Pixel sample을 위해서 할당된 비트의 수
7. Bits Stored (0028, 0101)
하나의 Pixel sample을 위해서 저장된 비트의 수
8. High Bit (0028,0102)
Bits allocated에서 Bits stored가 시작하는 부분
Image handling of DICOM 3.0 (계속)
Bit Stream
High Bit
Bits stored
Bits allocated

36. Image handling of DICOM 3.0 (계속)
* JPEG Encapsulation method
Encapsulation format은 UID로 구분한다.
* UID (Unique Identifier) : Data element tag (0002,0002)를 가지는 Data
element로써 하나의 속성을 나타내는 ISO가 인
정 한 숫자들의 조합

37. DICOM Toolkit
List of DICOM toolkit
http://www.schoech.de/diploma/toolkits.html
Selected toolkit list
ITK dicom
DCMTK
dcm4che
Imagectn – Implements DICOM Image Archive
Application Entity (Title, host, port)
Storage
Query
Retrieval

39. 53 Medical Imaging & Robotics Lab
Image Segmentation
 Meaning
– The partitioning of an image into nonoverlapping, constituent
regions that are homogeneous with respect to some
characteristics
• Intensity, texture, color, etc.
– For the image domain Ω,
1
K
k
k
S

 
where for ,and each is connectedk j kS S k j S 
Images from
R.C. Gonzalez et al., “Digital Image Processing (3rd ed.)”,
Pearson Prentice Hall, 2010

40. 54 Medical Imaging & Robotics Lab
Medical Image Segmentation
 Differences, Difficulties?
– Gray-level appearance of tissue
– Characteristics of imaging modality
• Different characteristics among MRI, CT, and other modalities
– Geometry of anatomy
 Applications
– Image Guided Surgery/Therapy
– Surgical Simulation
– Neuroscience Studies
– Therapy Evaluation
– Etc.
Mean wall shear stress
G Xiong, G. Choi, C. A. Talyor, “Virtual interventions for image-based blood
flow computation”, Computer-Aided Design, vol. 44, pp.3-14, 2012
Kapur, Tina. “Model based three dimensional medical
image segmentation.”MIT Ph.D. thesis, 1999

41. 55 Medical Imaging & Robotics Lab
Automated Medical Image Segmentation
 Limitations of Manual Segmentation
– Slow
• Up to 60 hours per scan
– Variable
• Up to 15% between experts
 The Automated Segmentation method is required!!
William (Sandy) Wells. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring2007
How can we segment this image manually in 3D??

42. 56 Medical Imaging & Robotics Lab
Automated Medical Image Segmentation
 Automated Segmentation - Approaches
– Thresholding
– Region-based Methods
• Region Growing
– Edge-based Methods
• Active contour models (snakes)
– Clustering
• K-means, etc.
– Graph Partitioning
– Statistical Models
• Active shape model, Bayesian approaches
– Atlas-based Methods
• Probabilistic atlas-based segmentation methods
• Non-probabilistic atlas-based segmentation methods

43. 57 Medical Imaging & Robotics Lab
Filtering
 Thresholding
– Global thresholding
• Choosing thresholds
– Using Prior knowledge
– Otsu’s Method:
– Isodata method
– Bayesian thresholding, etc
– Local thresholding
• Choosing thresholds
– Niblack thresholding
– Mardia & Hainsworth method
– Indicator Kriging, etc.
1 if ( )
( )
0 else
f
g x

 

x
2 2
0 1arg max ( )( ) ( )( )Otsu
k k
p k p k
 
 m m m m
 
 
    
 
 
1 if ( ) ( )
( )
0 else
f
g x

 

x x
original image Global hard thresholding
soft thresholding
Images from: AS. Aja-Fernandez et al., “Soft thresholding for
medical image segmentation”, 3nd Ann. Conf. IEEE EMBS, 2010

44. 58 Medical Imaging & Robotics Lab
Region-based Methods
 Region growing
– Start with set of seed pixels – region
– Iteratively include neighboring pixels that satisfy membership
criteria
• Intensity interval, Regional statistics, etc.
seed selectionimage growing result
original white matter gray matter
Image courtesy: ITK

45. 59 Medical Imaging & Robotics Lab
Edge-based Methods
 Active Contour Models (Snakes)
– Snake: the deformable curve that minimizes
Images from: A. Yezzi, S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, “A geometric snake model for
segmentation of medical imagery”, IEEE T. Medical Imaging, vol. 16(2), pp. 199-209, 1997
snake int ext image( )E E E E  
22 2
int 2
( ) ( )E s s
s s
 
 
 
 
u u
tension stiffness
ext :external attractive or repulsive energyE
image line edge termE E E E   original NMR image edge energy map

46. 64 Medical Imaging & Robotics Lab
Watershed Algorithm
 Topographic representation
– Visualize an 2D image in 3D – spatial coordinates and gray levels
– 3 types of points
Courtesy: ITK
Intensity
Water
Level
original
image
Topographic
view
Images from
R.C. Gonzalez et al., “Digital
Image Processing (3rd ed.)”,
Pearson Prentice Hall, 2010
• Points belonging to a regional
minimum
• Points at which a drop of water would
fall to a single minimum
• Points at which a drop of water would
be equally likely to fall to more than
one minimum

47. 65 Medical Imaging & Robotics Lab
Watershed Algorithm
 Examples
Results of further flooding Final watershed lines
Images from: R.C. Gonzalez et al., “Digital Image Processing (3rd ed.)”, Pearson Prentice Hall, 2010
Courtesy: ITK (Dr. J. Cates)
Watershed
transform

48. 66 Medical Imaging & Robotics Lab
Deformable Models
 Level set
– The mathematical model which describes the behavior of fronting
boundaries which is varying by time
• Implicit representation
• Introduced in the area of fluid dynamics by Osher-Sethian (1987)
Images courtesy: K. Siddiqi, et al., “Area and length minimizing flows for shape segmentation”, IEEE Trans. Imag. Proc., vol. 7, pp.433-443, 1998
0
t

  

v
( , )t x : level set
( , )tv x : vector field

49. 67 Medical Imaging & Robotics Lab
Clustering: K-means
 Method
– Step 1. Pick K cluster centers, either randomly or based on some
heuristic
– Step 2. Assign each pixel in the image to the cluster that minimizes
the variance between the pixel and the cluster center
– Step 3. Re-compute the cluster centers by averaging all of the
pixels in the cluster
– Step 4. Repeat steps 2 and 3 until convergence is attained
Segmentation
using the K-means
algorithm

50. 68 Medical Imaging & Robotics Lab
Statistical Shape Models (3D)
 Shape representation
– Training Set
• Landmarks and meshes
– k Landmarks of each training set:
– Shape variations (Shape model)
• : mean shape
• : eigen vectors of
1 1 1( , , , , , , )T
k k kx y z x y zx
1
c
m m
m
b 

  x x
1
1
( )( )
1
s
T
i i
is 
  

S x x x x
x
m
Principal modes of variation of the liver
T. Heiman, H.-P. Meinzer, “Statistical shape models for 3D medical image segmentation: A review”, Medical Image Analysis,
vol. 13, pp. 543-563, 2009
Iterative shape model search

51. 69 Medical Imaging & Robotics Lab
Case Example: Lung Segmentation
 Overall Procedure
Lung
Extraction
Lung
Separation
Smoothing
Smoothing
3D CT image
data
Left region
Right region
Smoothed
left region
Smoothed
right region
S. Hu, E. A. Hoffman, “Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-ray CT Images”, IEEE
Transactions on Medical Imaging, vol. 20(6), pp. 490-498, 2001
Left/Right lung
separation
Initial
segmentation
Original image
– Lung Extraction
• Threshold selection
• Connectivity and topological
Analysis
• Segmentation of the large
airways
– Lung Separation
• Morphological operations
– Smoothing
• Considering vessels, airways

52. 70 Medical Imaging & Robotics Lab
Case Example: Pulmonary Vessel Segmentation
Level-set approach
X. Ahu et al., “VOLES: Vascularity-oriented level set
algorithm for pulmonary vessel segmentation in image
guided intervention therapy”, Proc. IEEE International
Symposium on Biomedical Imaging, Boston, USA, 2009
Vessel model Intensity + tracing using 8 distance regions
H. Shikata et al.,
“Segmentation of pulmonary
vascular trees from thoracic
3D CT images”, International
Journal of Biomedical Imaging,
vol. 2009, Article: 636240
Multi-scaled opening result images
Z. Gao et al., “A new
paradigm of interactive
artery/vein separation in
noncontrast pulmonary CT
imaging using multiscale
topomorphic opening”, IEEE
Transactions on Biomedical
Engineering, vol. 59(11), pp.
3016-3027, 2012

53. 71 Medical Imaging & Robotics Lab
Semantic Segmentation
Courtesy of Jinwon Lee/CAPP lab., SNU

54. 93 Medical Imaging & Robotics Lab
Breast MR Segmentation
 Results
 Supine (dimension reduction : 5 times)

55. 3D Modeling
Surface
MIP
Min IP
Ray Sum
MPR
VE
Volume
Screen
Projection
Rendering Mode

56. MIP Min IP Ray Sum
3D Model Data
Rendering Mode
Surface
Ray Sum

57. 3D Model Generation
Surface Model Generation - Marching
Cubes
“Method of Generating Iso-Surfaces from a given Volume Data
Set”
Generates ‘Triangular Mesh’ by using primitive features
50 100
75
75
50
20
30
90
80
80
80

58. 3D Model Generation
Surface Model Generation - Decimation
“Process of eliminating excessive numbers of triangles in
a mesh”
h
1
2
3
46
5
점의 개수 : 7
삼각형 개수 : 6
1
2
3
4
점의 개수 : 6
삼각형 개수 : 4

59. Teeth & Socket

60. 3D Medical Visualization
Virtual EndoscopyVolume Rendering
V-Works (1998) **-> A-View Platform x86 (2004)
GPU based A-View Platform x64 (2010) :
MultiMask MultiOTF
OTF1 for Bone
OTF2 for Lung
Max 8 masks
**Kim N, et al, Stud Health Technol Inform, 1998. 52(2):1105-10.; CE Class IIa; FDA
Surface Rendering

61. 3D Medical Visualization; Neuron by
ECT*Electron Computed Tomography* in Neuroscie
Synapses (protein molecules in large
molecular machines)
Presynaptic terminal cytomatrix
*IJ You, N Kim, et al, APCET 2009
Image Acq.: 2° (-60° ~ +60°)
H-7650 system(Hitachi)
3D Recon :WBP and TBR algorithms by
EMIP*
Raw images
(*Electron Microscope Integrated Image Processing Software,
Hitachi)
Fetus using 3D Ultrasound
Tooth Structure using MicroCTMitochondria with island using TEM

62. 3DP procedure
Data Acquisition (Quality check)
• 3D volumetric images w/ isocubic voxel spacing is preferred.
• Higher SNR / Enhanced treatments for specific organs (vessel, DWI, cancer, …)
ROI segmentation
1) Thresholding 2) Seeded region-growing
3) Graph cut 4) Volume sculpting
5) Volume rendering 6) Manual editing & Calculating masks
3D Mesh generation & processing
A) Conversion to 3D mesh generation (stl, wrl , obj )
B) Full Color Texture C) Topological correction
D) Mesh triangle decimation D) Laplacian smoothing and local smoothing
E) Adding CAD parts F) Final check: Repair and fixing of 3D mesh
Printing & Post-processing
• Selection of appropriate printer and material, and then 3DP
• Chemical and/or physical post-processing (depend on machine and material)
• Optional treatments: grinding surface, coating or molding w/ clinical material,
etc.
• Sterilization
Multi-phase renal CT

63. SW for 3D modeling
 Open Source SW
- Meshmixer
- Meshlab
- FreeSurfer
 Commercial Source
- 3matics (Materialise Inc., Belgium)
- MagicsRP (Materialise Inc., Belgium)
Segmentation and surface modeling SW
 Open Source SW
- Slicer (www.slicer.org)
- itk-SNAP
 In-house Software
- Aview (AMC, MIRL)
 Commercial Software
- Mimics (Materialise Inc., Belgium)
Lobe segmentation Brain segmentation Brain parcellation
Kidney and RCC
segmentation
Mesh processing software

64. 의료용 3D 프린팅 서비스
의료용 3D 프린팅은 3D 프린터 기술과 의료 융합을 통해
환자 맞춤형 정밀의료 서비스를 제공
Imaging Segmentation Modeling Manufacturing
의료용 3D 프린팅의 프로세스
Surgery
Planning
CT/MRI 등으로
환자 영상 촬영
3D 이미지 中 필요
부분 분류 및 설계
Segment별 3D
형상 모델 생성
3D 프린터로
산출물 조형
환자 특성 고려한
수술 계획 수립
시뮬레이션 모형 가이드 삽입보형물
환자 맞춤형 정밀 의료 기기
교육용, 연구용 등

65. 데이터공유,워크플로우관리,Hybrid Rendering, Mobile Interface
환자 맞춤형
정밀의료기기
: 3D 프린팅 기술과 의료 융합을 통해 ‘환자 맞춤형 정밀의료 기기 임상적용’
3DP전용의료영상
획득
영상분할 STL모델링
3DP생산및
임상적용
3D프린팅 기반 첨단의료기기의 임상적용 프로세스
시술/수술계획
탑재
CT/MRI 등으로
환자영상촬영
3D이미지中필요
부분분류및설계
Segment별3D
형상모델생성
3D프린터로
산출물조형
선행
연구사례
@AMC
시뮬레이션 모형
가이드
삽입보형물
교육용, 연구용
등
[자체SW개발] [모델링기술] [실리콘3DP개발]
환자특성고려한
수술계획수립
[가상수술]
3D 프린팅 의료기기 기술개발 : ‘ 임상 현장, 정밀 의료 ’
111/27
바이오프린팅

66. Kidney RCC Modelling
EAU 2015, AUA 2015, RSNA 2015, WIP for publication, 비뇨기과 김청수, 경윤수 교수 협업

67. Surgical guide for partial nephrectomy
research phantom simulator for education/trainingpersonalized device (guide)

68. Eustachian tube ballooning stimulator
ObJet connex3
CJP (Projet 460)
research phantom simulator for education/trainingpersonalized device

69. Surgery rehearsal for Cardiovascular diseases
D. Yang, et al., Circulation: 132(4):300-301 (2015)

70. Personalized guide for breast conserving surgery
환자 별 의료영상 활용 종양 모델링 제거영역 모델링
수술가이드 모델링 Nipple 포함 모델링 3DP 수술가이드
맞춤형 적용 가이드를 이용한 marking 표시영역 (수술 자세)
최종 절개라인 표시 제거된 종양
dye injection marking 의료영상 기반 암 및 피부모델링
3DP 수술가이드 3DP 수술가이드 (앞면)
position
marker
Injection 가이드 모델링
hole for injection
nipple slot
for reference
injection
depth
가이드
3DP 수술가이드
needle injection
simulation
3DP 수술가이드 (뒷면)
hole for injection
needle injection simulation
임상적용 사례
research phantom simulator for education/trainingpersonalized device (guide)
Line marking type Hybrid type with injection marking

71. In-vitro study for stent abutment effect
wall thickness: 2 mm
length: 40 mm
diameter: 18 mm
modified
STL. 3DPCTG
PC-SA
liquid diet
FC-SA
soft diet
FC-NSA
soft diet
PC-SA
solid diet
FC-SA
solid diet
[Results] radiographs analysis
research phantom simulator for education/trainingpersonalized device

72. 기도암/폐암 모델
환자설명용, 호흡기내과 최창민, 영상의학과 김미영, RSNA 2015, 박태선 선생님 신진과제

73. Hard tissue model : C-Spine (low dose CT)

74. Dental Application

75. 흉강경 팬텀

76. patient
specific case
leaflet
calcificationaorta
TAVI 시뮬레이터 >> 생체모사 !
Edward Inc.

77. RSNA 2016 4 Presentations

79. RSNA 2016 4 Presentations

80. 126 3D printing human
Whole body MRI : 1.3mm^3
Brain Modeling
뇌 유동 팬텀
(김종성, 김범준)
코 가이더
(고경석)
Eustachian Tube
(박홍주)
흉강경 팬텀 (김용희)
척추측만증 :
(하정기, 조제환)
폐 보형물 (최세훈)
기도 Simulation
(신지훈, 송호영)
기도폐암 modeling
(최장민, 김미영)
기도스텐트
(최장민)
Temporal bone
(이환서)
Denture
C-spine
(정형)
신장암
시뮬레이터
(김청수, 경윤수)
신장 결석
(박형근, 경윤수)
유방암Guide
(안세형, 이종원, 고범석)
유방보형물
(고범석)
OMFS/PS 수술 시뮬레이터
비혈관 스텐트
(송호영)
실리콘 3D 프린터
세라믹
3D 프린터
골접합 보조
생적합 매쉬 (정형)
L-spine
TAVI 모형
(김영학, 양동현)
(Medtronics사)
대동맥 협착 팬텀
(S&G bio사)
IBI Simulator
(송호영)
AVM 모델
(이덕희)
HCMP Modeling
(주석중, 양동현)
위십이지장 팬텀
(송호영)
자궁경부암 Modeling
(김대연)
대동맥
dissection 팬텀
(양동현, 김준범)
혈관 중재 팬텀
(김영학, 남기병)
관절경 Modeling
(김종민)
신장암 수술 Guide
(김청수, 경윤수)
마우스 헬멧 (김준기)
안와골절 Implant
guide (사호석)
3DP
Guide(curve)
Implant(flat)
(실리콘 몰딩)
(콜라겐 몰딩)
감사합니다