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영상기반 딥러닝 의료응용
2017.10.27
한국과학기술연구원 의공학연구소
김 영 준
Contents
 연구소개
 딥러닝 의료응용 사례
 딥러닝 배우기
 딥러닝 의료응용 (KIST 연구)
2
3
3D 의료영상 처리 S/W
4
▶ 3D 가상 수술
▶ 3D 환자모델링
▶ 3D 형상 모델 처리기술
3D Patient Modeling
5
3D Surgical Planning
6
7
Contents
 연구소개
 딥러닝 의료응용 사례
 딥러닝 배우기
 딥러닝 의료응용 (KIST 연구)
8
Google's AlphaGo Zero destroys humans all on its own
9
10
AlphaGo Zero
11
Google, 안과 전문의 수준의 AI 기술 (JAMA, 2016.11)
12
Google, 안과 전문의 수준의 AI 기술 (JAMA, 2016.11)
13
14
15
Deep Learning 기반 피부 질환 진단 (Nature, 2017.2)
 Stanford Univ. / 21명 피부과 전문의와 진단 성능 동등함 증명
16
https://brunch.co.kr/
@kakao-it/81
뷰노 CTO 정규환 박사
17
G. Litjens, et al. A Survey on Deep Learning in
Medical Image Analysis, arXiv, 2017
18
19
X.Wang et al. ChestX-ray8: Hospital-scale Chest X-ray Database and
Benchmarks on Weakly-Supervised Classification and L...
https://chuckyee.github.io/cardiac-segmentation/
D. Nie, et al. Medical Image Synthesis with Context-Aware
GAN (GenerativeAdversarial Networks), 2016
21
Z. Li, et al. Deep Learning based Radiomics (DLR) and its
usage in noninvasive IDH1 prediction for low grade glioma,
Scien...
C.M. Deniz, et al. Segmentation of the Proximal Femur from MR
Images using Deep Convolutional Neural Networks, arXiv, 2017...
24
25
[‘VUNO’ & 서울아산병원의 딥러닝 적용 폐질환 진단 소프트웨어]
[AI기반 의료영상 진단기업 ‘Lunit’, 세계 100대 AI startup 선정]
Bone Age 측정
26
 MGH
 VUNO
NIH, 100,000장의 Chest X-ray 영상 데이터셋 공개
 The dataset of scans is from more than 30,000 patients,
including many with advanc...
Grand Challenges in Biomedical Image Analysis
https://grand-challenge.org/All_Challenges/
28
The Cancer ImagingArchive (TCIA) collections
http://www.cancerimagingarchive.net/
29
Contents
 연구소개
 딥러닝 의료응용 사례
 딥러닝 배우기
 딥러닝 의료응용 (KIST 연구)
30
31
Deep Learning Online Lecture
1. Coursera / Deep Learning Specialization (유료)
https://www.coursera.org/specializations/deep...
Deep Learning Online Lecture
1. Coursera / Deep Learning Specialization
https://www.coursera.org/specializations/deep-lear...
34
Python
https://www.codecademy.com/learn/learn-python
Jupyter Notebook
http://jupyter.org/
The Jupyter Notebook is an open-source web application that allows you to
create and ...
Python
http://i-
systems.github.io/HSE545/machine%20learning%20all/Workshop/KSME/00_
basic_python.html
딥러닝 개요
38
https://www.slideshare.net/yongho/ss-79607172
Deep Learning Papers Reading Roadmap
https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
39
Contents
 연구소개
 딥러닝 의료응용 사례
 딥러닝 배우기
 딥러닝 의료응용 (KIST 연구)
40
AI기반 3D 의료용 S/W 기술
41
임상 빅데이터
환자 3D 데이터
AI 진단
Classification
AI 치료 계획
치료방법 추천
AI 치료 분석
수술결과 분석
3D CNN 기반 Rotator Cuff Tear 진단
42
 회전근개 MRI (fat suppression)
 정상 / 파열 두 가지 클래스로 분류
 정상 데이터 710명 (N=~2000), 파열 환자 1138명...
3D CNN 기반 Rotator Cuff Tear 진단
43
 학습
 Voxception-ResNet 네트워크 사용, epoch 110
 Train set 3573개 (None-RCT : 1749, RCT : 18...
3D CNN 기반 Rotator Cuff Tear 진단
44
AI 기반 수술 계획 S/W
45
 3D 수술계획 자동수립
Ex) 양악수술 자동 3D수술계획 추천, 랜드마크 자동 입력 및 분석
임상 빅데이터
환자 3D 데이터
AI 진단
Classification
AI 치료 계획
치...
Deep Q Learning 기반 좌표계 정합
46
† 2017 ACDDE 국제학회, Best Paper Award
Book - VTK 프로그래밍
47https://github.com/vtk-book/example 47
48
Chapter4. DICOM Viewer 제작 (고급 응용 프로그램 예
제)
4-1 DICOM Viewer 소개 -124
4-2 프로젝트 생성 및 환경 설정 -128
4-3 4분할 윈도우 구성 -144
4-4 VT...
Summary
딥러닝 의료응용 사례
딥러닝 배우기
딥러닝 의료응용 (KIST 연구)
Thank you!
50
 Contact Information
Youngjun Kim, Ph.D.
- Center for Bionics, Korea Institute of Science and Technology
- ...
Journal Paper Publication (2016~)1. S.W. Chung, …, Y. Kim, "Serial Changes in 3-Dimensional Supraspinatus Muscle Volume fo...
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영상기반 딥러닝 의료 분야 응용 (KIST 김영준) - 2017 대한의료영상학회 발표

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영상기반 딥러닝 의료 분야 응용 (KIST 김영준) - 2017 대한의료영상학회 발표

  1. 1. 영상기반 딥러닝 의료응용 2017.10.27 한국과학기술연구원 의공학연구소 김 영 준
  2. 2. Contents  연구소개  딥러닝 의료응용 사례  딥러닝 배우기  딥러닝 의료응용 (KIST 연구) 2
  3. 3. 3
  4. 4. 3D 의료영상 처리 S/W 4 ▶ 3D 가상 수술 ▶ 3D 환자모델링 ▶ 3D 형상 모델 처리기술
  5. 5. 3D Patient Modeling 5
  6. 6. 3D Surgical Planning 6
  7. 7. 7
  8. 8. Contents  연구소개  딥러닝 의료응용 사례  딥러닝 배우기  딥러닝 의료응용 (KIST 연구) 8
  9. 9. Google's AlphaGo Zero destroys humans all on its own 9
  10. 10. 10
  11. 11. AlphaGo Zero 11
  12. 12. Google, 안과 전문의 수준의 AI 기술 (JAMA, 2016.11) 12
  13. 13. Google, 안과 전문의 수준의 AI 기술 (JAMA, 2016.11) 13
  14. 14. 14
  15. 15. 15
  16. 16. Deep Learning 기반 피부 질환 진단 (Nature, 2017.2)  Stanford Univ. / 21명 피부과 전문의와 진단 성능 동등함 증명 16
  17. 17. https://brunch.co.kr/ @kakao-it/81 뷰노 CTO 정규환 박사 17
  18. 18. G. Litjens, et al. A Survey on Deep Learning in Medical Image Analysis, arXiv, 2017 18
  19. 19. 19 X.Wang et al. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, CVPR, 2017
  20. 20. https://chuckyee.github.io/cardiac-segmentation/
  21. 21. D. Nie, et al. Medical Image Synthesis with Context-Aware GAN (GenerativeAdversarial Networks), 2016 21
  22. 22. Z. Li, et al. Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma, Scientific Reports, 2017 22
  23. 23. C.M. Deniz, et al. Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks, arXiv, 2017 23
  24. 24. 24
  25. 25. 25 [‘VUNO’ & 서울아산병원의 딥러닝 적용 폐질환 진단 소프트웨어] [AI기반 의료영상 진단기업 ‘Lunit’, 세계 100대 AI startup 선정]
  26. 26. Bone Age 측정 26  MGH  VUNO
  27. 27. NIH, 100,000장의 Chest X-ray 영상 데이터셋 공개  The dataset of scans is from more than 30,000 patients, including many with advanced lung disease. 27 https://nihcc.app.box.com/v/ChestXray-NIHCC
  28. 28. Grand Challenges in Biomedical Image Analysis https://grand-challenge.org/All_Challenges/ 28
  29. 29. The Cancer ImagingArchive (TCIA) collections http://www.cancerimagingarchive.net/ 29
  30. 30. Contents  연구소개  딥러닝 의료응용 사례  딥러닝 배우기  딥러닝 의료응용 (KIST 연구) 30
  31. 31. 31
  32. 32. Deep Learning Online Lecture 1. Coursera / Deep Learning Specialization (유료) https://www.coursera.org/specializations/deep-learning 2. Udacity / Deep Learning https://classroom.udacity.com/courses/ud730 3. Udacity / Deep Learning Nanodegree Foundation (유료) https://classroom.udacity.com/nanodegrees/nd101/syllabus/core- curriculum 4. 모두를 위한 딥러닝 – 기본적인 머신러닝과 딥러닝 강좌 https://www.inflearn.com/course/기본적인-머신러닝-딥러닝-강좌/ 5. CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/
  33. 33. Deep Learning Online Lecture 1. Coursera / Deep Learning Specialization https://www.coursera.org/specializations/deep-learning
  34. 34. 34
  35. 35. Python https://www.codecademy.com/learn/learn-python
  36. 36. Jupyter Notebook http://jupyter.org/ The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.
  37. 37. Python http://i- systems.github.io/HSE545/machine%20learning%20all/Workshop/KSME/00_ basic_python.html
  38. 38. 딥러닝 개요 38 https://www.slideshare.net/yongho/ss-79607172
  39. 39. Deep Learning Papers Reading Roadmap https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap 39
  40. 40. Contents  연구소개  딥러닝 의료응용 사례  딥러닝 배우기  딥러닝 의료응용 (KIST 연구) 40
  41. 41. AI기반 3D 의료용 S/W 기술 41 임상 빅데이터 환자 3D 데이터 AI 진단 Classification AI 치료 계획 치료방법 추천 AI 치료 분석 수술결과 분석
  42. 42. 3D CNN 기반 Rotator Cuff Tear 진단 42  회전근개 MRI (fat suppression)  정상 / 파열 두 가지 클래스로 분류  정상 데이터 710명 (N=~2000), 파열 환자 1138명 (N=~2000)  데이터 프로세싱 및 진단 S/W  Dicom 파일 loading  orientation 정보로 자동 정렬  Proximal humerus의 volume crop & interpolation (64x64x64)  Preprocessing & saving of labeled data
  43. 43. 3D CNN 기반 Rotator Cuff Tear 진단 43  학습  Voxception-ResNet 네트워크 사용, epoch 110  Train set 3573개 (None-RCT : 1749, RCT : 1824) , Test set 200개  결과  Test set에서 약 95퍼센트 정확도 진단 학습 진행에 따른 정확도 및 히트맵 변화Voxception-ResNet
  44. 44. 3D CNN 기반 Rotator Cuff Tear 진단 44
  45. 45. AI 기반 수술 계획 S/W 45  3D 수술계획 자동수립 Ex) 양악수술 자동 3D수술계획 추천, 랜드마크 자동 입력 및 분석 임상 빅데이터 환자 3D 데이터 AI 진단 Classification AI 치료 계획 치료방법 추천 AI 치료 분석 수술결과 분석
  46. 46. Deep Q Learning 기반 좌표계 정합 46 † 2017 ACDDE 국제학회, Best Paper Award
  47. 47. Book - VTK 프로그래밍 47https://github.com/vtk-book/example 47
  48. 48. 48 Chapter4. DICOM Viewer 제작 (고급 응용 프로그램 예 제) 4-1 DICOM Viewer 소개 -124 4-2 프로젝트 생성 및 환경 설정 -128 4-3 4분할 윈도우 구성 -144 4-4 VTK Window 초기화 -154 4-5 DICOM 파일 읽기 168 4-6 Volume 데이터 읽기 및 렌더링 -206 부 록 1 VTK 설치법 252 2 GDCM 설치법 -259 3 주요 DICOM 태그 -267 4 기타 VTK 프로그래밍 팁 268
  49. 49. Summary 딥러닝 의료응용 사례 딥러닝 배우기 딥러닝 의료응용 (KIST 연구)
  50. 50. Thank you! 50  Contact Information Youngjun Kim, Ph.D. - Center for Bionics, Korea Institute of Science and Technology - E-mail: junekim@kist.re.kr - Tel.: 02-958-5606 - C.P.: 010-5234-5378
  51. 51. Journal Paper Publication (2016~)1. S.W. Chung, …, Y. Kim, "Serial Changes in 3-Dimensional Supraspinatus Muscle Volume following Rotator Cuff Repair", The American Journal of Sports Medicine, Vol. 45, No. 10, Aug. 2017 2. Y.D. Choi*, Y. Kim*, E. Park, “Patient-Specific Augmentation Rhinoplasty Using a Three-Dimensional Simulation Program and Three-Dimensional Printing”, Aesthetic Surgery Journal, DOI: 10.1093/asj/sjx046, May, 2017 3. S. Kim, D. Lee, S. Park, K. Oh, S.W. Chung, Y. Kim, "Automatic segmentation of supraspinatus from MRI by internal shape fitting and autocorrection", Computer Methods and Programs in Biomedicine, Vol. 140, pp. 165-174, Mar. 2017 4. Y. Kim, B.H. Lee, K. Mekuria, H. Cho, S. Park, J.H. Wang, D. Lee, "Registration accuracy enhancement of a surgical navigation system for anterior cruciate ligament reconstruction: A phantom and cadaveric study", The Knee, S0968-0160(16)30248-4, Feb. 2017 5. C. Kyu Lee, Y. Kim, N. Lee, B. Kim, D.Y. Kim, S. Yi, "Feasibility study of utilization of action camera, GoPro Hero 4, Google glass and Panasonic HX-A100 in Spine surgery", Spine, Vol. 42, No. 4, pp. 275-280, Feb. 2017 6. Q.C. Nguyen, Y. Kim, H. Kwon, "Optimization of layout and path planning of surgical robotic system", Int’l Journal of Control, Automation and Systems, Vol. 15, No. 1, pp. 375-384, Jan. 2017 7. Q.C. Nguyen*, Y. Kim*, S. Park, H. Kwon, "End-effector path planning and collision avoidance for robot-assisted surgical system", Int’l Journal of Precision Engineering and Manufacturing, *These authors contributed equally to this work, Vol. 17, No. 12, Dec. 2016 8. B.H. Lee, D.H. Kum, I.J. Rhyu, Y. Kim, H. Cho, J.H. Wang, "Clinical advantages of image-free navigation system using surface-based registration in anatomical anterior cruciate ligament reconstruction", Knee Surgery, Sports Traumatology, Arthroscopy, Vol. 24, No. 11, pp. 3556-3564, Nov. 2016 9. J.G. Seo*, S.M. Kim, J.M. Shin, Y. Kim*, B.H. Lee, "Safety of simultaneous bilateral total knee arthroplasty using an extramedullary referencing system: results from 2098 consecutive patients", Archives of Orthopaedic and Trauma Surgery, Vol. 136, No. 11, pp. 1615-1621, Nov. 2016 10. Y. Kim, Y.H. Na, L. Xing, R. Lee, S. Park, "Automatic deformable surface registration for medical applications by radial basis function-based robust point-matching", Computers in Biology and Medicine, Vol. 77, No. 1, pp. 173-181, Oct. 2016 11. S.H. Park, S.W. Moon, B.H. Lee, S. Park, Y. Kim, D. Lee, S. Lim, J.H. Wang, "Arthroscopically blind anatomical anterior cruciate ligament reconstruction using only navigation guidance: a cadaveric study", The Knee, DOI: 10.1016/j.knee.2016.02.020, Jul. 2016 (Epub ahead of print) 12. Y. Kim, W. Kim, D. Lee, "3D Inspection by Registration of CT and Dual X-ray Images", Journal of Int’l Society for Simulation Surgery, Vol. 3, No. 1, pp. 16-21, Jun. 2016 13. J.P. Yoon, S.W. Chung, J. Kim, H.S. Kim, H.J. Lee, W.J. Jeong, K.S. Oh, D.O. Lee, A. Seo, Y. Kim, "Intra-articular injection, subacromial injection, and hydrodilatation for primary frozen shoulder: a randomized clinical trial", Journal of Shoulder and Elbow Surgery, Vol. 25, No. 3, pp. 376-383, Mar. 2016 14. J. Charton, L. Kim, Y. Kim, "Boolean operations by a robust, exact, and simple method between two colliding shells". Journal of Advanced Mechanical Design, Systems, and Manufacturing, (Accepted) 15. E. Shim, Y. Kim, D. Lee, B.H. Lee, S. Woo, K. Lee, “2D-3D registration for 3D analysis of lower limb alignment in a weight-bearing condition”, Applied Mathematics – Journal of Chinese Universities (Accepted) 51

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