Large scale image database in orthopaedic surgery

1. Construction and Application of Large‐scale
Image Database in Orthopaedic Surgery
Yoshito Otake
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST), Japan
Imaging-based
Computational
Biomedicine Lab
June 10th , 2016
CAOS 2016, Osaka
Panel Discussion

2. Construction of Large‐scale Image Database
• Database of CT, Radiographs
and surgical navigation logs
• 1147 cases of hip surgery
operated from 2005 to 2015
• Contains CTs of four‐phases for
70 patients, which is useful for
the analysis of pathology
progression and therapeutic
recovery
• Anatomical structures were
reconstructed using automated
segmentation algorithms
Collaboration with Dr. Nobuhiko Sugano
and Dr. Masaki Takao, Osaka University
Before surgery After surgery
of one side
Before surgery of
the other side
After surgery of
the other side

3. Automated Segmentation of Hip Joint with Pathology
Unaffected hip
Primary
osteoarthritis
Secondary
osteoarthritis
( Crowe 1)
Secondary
osteoarthritis
( Crowe 2)
Collapsed
hip
[Yokota, MICCAI 2013]

4. 1. Globally consistent initial segmentation using hierarchical hip SSM
2. Accurate segmentation of joint part using conditional SSMs
3. Final refinement by graph cut
Approaches of Bone Segmentation
Hierarchical hip SSM
Conditional femoral head SSM
[Yokota, MICCAI 2013]
More Accurate
More Robust
Specificity
>
Generality
<
[de Bruijne MICCAI 2006]
[Okada, MICCAI 2007]

5. Automated Segmentation of Muscles
Pelvis & Femur Muscle tissues 17 Muscles
Different patients
Hierarchical multi‐atlas method
provided accuracy of 1.43 mm
(against manual segmentation)
in 19 hip and thigh muscles
[Yokota, CAOS 2012]

6. (1) Statistical modeling of standing position
Potential Application Areas
Analysis of joints in standing position
provides useful information in surgical
planning
(2) Statistical modeling of muscle
anatomy and function
Patient‐specific variation in muscle
fiber arrangement can be statistically
modeled using CT
Digitally
reconstructed
radiograph of
gluteus
maximus
Patient #1 Patient #2 Patient #3
Fiber
orientation
estimated by
structure
tensor analysis
(3) Statistical modeling of surgeon’s
expertise
Surgeon’s expertise contained in the
surgical record database can be
statistically modeled to generate new
(better) surgical plans
Automatically
generated
patient‐
specific
surgical plan

7. CT (Supine)
(1) Statistical modeling of standing position: Materials
Summary of our dataset
Clinical collaborator Osaka Univ. Hospital, Dept. of Orthopaedic Surgery
Study population Patients who are subjected to hip surgery
Number of case 475 cases（Male:69, Female:406, Average age：59 y.o.）
Type of images
Radiograph : PA view in standing position
CT : supine position
Dimension and voxel size
Radiograph: Approx. 2900 x 2900 [pixels] (0.13 x 0.13 [mm2])
CT: Approx. 512 x 512 x 550 [voxels] (0.7 x 0.7 x 1 [mm3])
Radiograph (Standing)

8. (1) Statistical modeling of standing position: Method
CT image
(supine)
Segmentation of pelvis and
localization of landmarks
Radiograph
(standing)
Automated Segmentation
(Yokota et al, MICCAI 2009)
Registration of pelvis
and landmarks
PSI in supine
PSI in standing
Comparison
2D‐3D registration
(Otake et al, TMI 2012)
（Otake et al, PMB 2015）
Similarity : Gradient correlation
Optimizer : CMA‐ES
• Using hierarchical
statistical shape model

9. • degree posterior inclination from supine to standing.
(1) Statistical modeling of standing position: Results
PSI
in
standing
[deg]
PSI in supine [deg]
‐50
‐30
‐10
10
30
50
‐50 ‐30 ‐10 10 30 50
diagonal
A more detailed clinical study will be presented in
Session 26 (Sat, 10:50) by Dr. Uemura
Further potential application scenarios
• Positions other than standing
• Motion
Radiograph DRR
Sitting
Results of 475 cases

10. Optical image (0.1mm^3/voxel) Clinical CT image (1.0mm3/voxel)
Visible Korean Human: VKH (Cadaver data)
Fascicle (Bundle
of muscle fiber)
Muscle fiber
（10～100μm）
myofibril
From Wikipedia
(2) Statistical modeling of muscle anatomy and function
Collaborating with
Prof. Chung (Ajou Univ.
Korea)

11. (2) Statistical modeling of muscle anatomy and function
Fascicle (Bundle
of muscle fiber)
Muscle fiber
（10～100μm）
myofibril
From Wikipedia
Automated
segmentation
Clinical CT
Estimated fiber bundle
local orientation from VKH
CT of Visible Korean Human (VKH)
Will be validated using
Visible Korean optical images.
Digitally Reconstructed Radiograph
(DRR) of segmented muscle
Visible Korean Human: VKH (Cadaver data)
Collaborating with
Prof. Chung (Ajou Univ.
Korea)

12. Training Database
Automated Muscle Fiber Arrangement
and Attachment Area Estimation
Statistical Model of
Functional Anatomy
e1
eN
e2 v2
v1
e3
Gluteus
maximus
Gluteus
medius
Patient‐specific
functional anatomy
Target
patient data
(2) Statistical modeling of muscle anatomy and function

13. (3)Statistical Modeling of Surgeon’s Expertise
Prior probability of likely spatial relations
between patient bone and implant
Patient Femoral
Cavity Shape Data: D
Stem Plan
Femoral Cavity ‐ Stem Statistical
Model P(Xfemur, Xstem)
Surgical Plan
Database
Automated Planning
Maximize P(Xfemur, Xstem)P(D|Xfemur)
Statistical Distance Map (SDM)
penetration 0 gap
[Otomaru et al. Med Image Anal 2012]
[Takao CAOS 2013]
Maximum a Posterior (MAP) Estimation

14. • The automated surgical planning was
tested and evaluated on 208 patient
datasets (including CT, implant CAD
and surgeon’s plan)
• Detailed evaluation (and publication)
is underway
(3)Statistical Modeling of Surgeon’s Expertise
Criteria AutoImPlan(Auto size)
Cup coverage [%] 87.1±8.4
LLD(ABS) [mm] 1.70±3.12
ROM(f90ip) [deg.] 46.5±7.7
ROM(f0im) [deg.] 43.0±5.3
ROM(f0fp) [deg.] 132.1±3.2
ROM(f0fm) [deg.] 40.9±7.1
Cup plan atlas 1.51±1.4
Summary of joint function parameters
Evaluation with large‐scale database

15. • Statistical shape modeling: state‐of‐the‐art or state‐of‐the‐practice?
– Depends on the target anatomy, bones: State‐of‐the‐practice, muscles and muscle
fibers: State‐of‐the‐art
• Where and how statistical shape modeling can be applied in CAOS?
– Segmentation of skeletal structures with pathology
– Modeling of pathological progression
• Statistical shape models vs. individual 3D CT images: when and where?
– No question about when/where. Always BOTH.
– Anatomical prior (SSM) is going to be always used in CT scanning (reconstruction) to
achieve “super‐low‐dose CT scan”
• Statistical shape models and patient‐specific measurements: race, gender, age,
and pathology considerations
– Prior classification by these factors should help reducing variation in training dataset
• Statistical shape models and atlases: are they same?
– Atlas is a labeled image, SSM represents statistical variation.
– Atlas contains identifiable information while SSM does not.
Discussion and Conclusion
SSM
Atlases

16. Acknowledgements
ICB Laboratory
Imaging-based Computational Biomedicine
http://icb-lab.naist.jp/
Advisors
Yoshinobu Sato (NAIST)
Akihiko Uchiyama (Waseda Univ.)
Russell Taylor (JHU, CS)
Mehran Armand (JHU, ME)
Jeffrey Siewerdsen (JHU, BME)
Greg Hager (JHU, CS)
Clinical Collaborators
Nobuhiko Sugano (Osaka Univ, Orthopedics)
Masaki Takao (Osaka Univ, Orthopaedics)
Engineering Collaborators
Futoshi Yokota (NAIST)
Ryan Murphy (JHU, APL)
Robert Grupp (JHU, CS)
J Web Stayman (JHU, BME)
Funding Support
JST PRESTO, JSPS Grant-in-Aid for Scientific
Research on Innovative Areas