## Project Goals

The sizes of current off-the-shelf humerus implants are unable to accommodate Asian patients since they are mainly produced for American and European populations according to locally collected data. By creating statistical humerus atlases based on Asian data, gender-specific and region-specific humeral implants can be developed by considering the characteristics of the statistical atlas constructed in order to improve stability of the fixation and avoid related complications. Besides, it is envisioned that the statistical atlas can serve as a critical reference for development and evaluation of robots in surgical procedures. Particularly, for the surgical and interventional procedures in the confined and rotated intramedullary space, the curvature and shape statistics of internal humerus canal is of great significance for the dedicatedly design of snake-like curvilinear tubular robot.

In this project, an efficient way has been demonstrated to construct statistical atlas by adopting an efficient alignment algorithm with improved efficiency and good accuracy. The constructed humerus atlas is then regarded as the reference for design of various humerus implants and development of snake-like concentric tube robots.

## Approach

Statistical Atlas Construction: A three-step algorithm is adopted in statistical atlas construction, including segmentation, alignment and principal component analysis (PCA). Segmentation is to extract the desired surface mesh information of the humeri from the raw CT data and alignment is to align all the samples. The final step is to perform the principal component analysis of the shapes and represent the statistical model using principal components.

Centerline Extraction for Intramedullary Robot Design: The Laplacian-Based Contraction Method is adopted to extract the centerline of the humerus atlas. The purpose is to explore the intramedullary structure of the humerus since the curvature and shape statistics of internal humerus canal is significant for the design of snake-like curvilinear tubular robot.

Curvature Analysis for Design of Humerus Implants: The maximum principal curvature is depicted in the below Figure. The curvature analysis is to study the statistical surface curvature of the humerus atlas, in order to assist the design of humerus implants such as proximal and distal humerus locking plates, both used in orthopaedic trauma fixation.

## Current Results

By adopting the novel atlas construction algorithm, the statistical humerus atlas is constructed as shown in the below figure, where the shape variation is along the first three principal components (PCs) and each row is generated by varying the shape with -3 to +3 standard deviations.

The variation along the first principal component is shown here (click to view the animation). From -3std to +3std, the length of the humerus model is increased while the width is decreased.

Moreover, by analyzing the characteristics of the humerus atlas, the intramedullary continuum robot design and the proximal humerus locking plate are depicted in the following figures.

## People Involved

Staff: Keyu WU

Advisor: Hongliang REN

Clinicians: Keng Lin Wong, Zubin Jimmy Daruwalla, Diarmuid Murphy, National University Hospital

## Publications

- Wu K, Wong FKL, Ng SJK, Quek ST, Zhou B, Murphy D, Daruwalla ZJ and Ren H (2015), “Statistical atlas-based morphological variation analysis of the asian humerus: Towards consistent allometric implant positioning”, International Journal of Computer Assisted Radiology and Surgery. Vol. 10(3), pp. 317-327. Springer Berlin Heidelberg
- Wu K, Daruwalla ZJ, Wong FKL, Murphyand D and Ren H (2015), “Development and Selection of Asian-specific Humeral Implants based on Statistical Atlas: Towards Planning Minimally Invasive Surgery”, International Journal of Computer Assisted Radiology and Surgery. Vol. 10(8), pp. 1333-1345. Springer Berlin Heidelberg.
- Wu, K.; Wong, F. K. L.; Daruwalla, Z. J.; Murphy, D. & Ren, H. Statistical Humerus Atlas for Optimal Design of Asian-Specific Humerus Implants and Associated Intramedullary Robotics, ROBIO 2013, IEEE International Conference on Robotics and Biomimetics, 2013; (Best Paper Finalist award)

## Supporting materials

Supplementary information for

**Statistical Atlas Based Morphologic Variation Analysis of the Asian Humerus: Towards Consistent Allometric Implant Positioning**

K. Wu, K. L. Wong, S. J. K. Ng, S. T. Quek, B. Zhou, D. P. Murphy, Z. J. Daruwalla, H. Ren*

All data are published in .nii format, which can be opened by ITK-SNAP (http://www.itksnap.org/pmwiki/pmwiki.php) and 3D Slicer (http://www.slicer.org/).

Table 1. Information of the subjects.

Study Number |
Male/Female |
Age |
Race |
Weight (kg) |
Height (cm) |
Exercise habit: S = sedentary, A = active |
Left/Right: L = left, R = right |

5260823 |
F |
61 |
C |
67.9 |
143 |
S |
L |

5190438 |
M |
16 |
C |
55.6 |
160 |
A |
R |

5186672 |
M |
63 |
C |
67.5 |
160 |
A |
L |

5172526 |
M |
50 |
C |
61.0 |
170 |
S |
L |

5018737 |
F |
77 |
C |
57.9 |
151 |
S |
R |

4636229 |
M |
54 |
C |
42.8 |
161 |
S |
L |

4585165 |
M |
38 |
C |
60.3 |
171 |
A |
L & R |

4481609 |
F |
83 |
C |
60.1 |
149 |
S |
L |

4448930 |
F |
49 |
I |
73.7 |
150 |
S |
R |

4320030 |
M |
69 |
C |
63.7 |
163 |
S |
L |

4111802 |
M |
63 |
C |
57.4 |
160 |
S |
L |

3969853 |
F |
61 |
C |
64.3 |
157 |
A |
R |

3564131 |
M |
19 |
C |
97.5 |
167 |
A |
L |

3495415 |
M |
74 |
M |
57.1 |
175 |
S |
L |

3827904 |
M |
33 |
M |
64.2 |
170 |
A |
L |

3722692 |
F |
73 |
I |
57.3 |
151 |
S |
L |

3768879 |
M |
20 |
C |
65.0 |
170 |
A |
L |

3771777 |
F |
74 |
C |
53.3 |
165 |
S |
L & R |

3159961 |
M |
51 |
C |
60.0 |
170 |
A |
L |

3273883 |
F |
80 |
C |
54.8 |
155 |
S |
L |

3384844 |
F |
71 |
C |
53.1 |
155 |
S |
R |

3353378 |
F |
78 |
M |
40.0 |
140 |
S |
L |

3271006 |
F |
59 |
M |
60.0 |
151 |
S |
L |

3189139 |
M |
47 |
C |
72.0 |
179 |
A |
R |

3132590 |
M |
22 |
M |
79.0 |
168 |
A |
L |

3143094 |
M |
23 |
C |
60.6 |
162 |
A |
R |

3282457 |
F |
90 |
C |
41.2 |
150 |
S |
L |

3296805 |
M |
57 |
M |
61.2 |
160 |
S |
L |

3326396 |
M |
21 |
C |
76.0 |
175 |
A |
L |

3338591 |
F |
61 |
C |
56.0 |
153 |
S |
L & R |

3060290 |
M |
21 |
M |
73.0 |
160 |
A |
L |

5581085 |
F |
49 |
M |
81.9 |
159 |
S |
L & R |

5691045 |
F |
45 |
C |
45.1 |
161 |
A |
L |

5731852 |
F |
87 |
C |
58.2 |
150 |
S |
R |

5733267 |
F |
57 |
C |
56.4 |
142 |
S |
L & R |

5622431 |
M |
64 |
I |
52.9 |
162 |
A |
R |

6007641 |
M |
44 |
I |
74.2 |
165 |
A |
L |

6013682 |
F |
74 |
C |
52.0 |
153 |
S |
R |

5504982 |
F |
24 |
C |
54.0 |
153 |
A |
L |

Fig. 1. Statistical male humerus atlas (left). The shape variation is along the first three principal components (PCs) and each row is generated by varying the shape with standard deviations ranging from -3√(λ_k ) to +3√(λ_k ).

Fig. 2. Statistical female humerus atlas (left). The shape variation is along the first three principal components (PCs) and each row is generated by varying the shape with standard deviations ranging from -3√(λ_k ) to +3√(λ_k ).

## References

[1] H. Ren, N. V. Vasilyev, and P. E. Dupont, “Detection of curved robots using 3d ultrasound,” in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011, pp. 2083–2089.

[2] H. Ren and P. E. Dupont, “Artifacts reduction and tubular structure enhancement in 3d ultrasound images,” in International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011.

[3] G. Chintalapani, L. M. Ellingsen, O. Sadowsky, J. L. Prince, and R. H. Taylor, “Statistical atlases of bone anatomy: construction, iterative improvement and validation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007. Springer, 2007, pp. 499–506.

[4] H. Ren and P. E. Dupont, “Tubular enhanced geodesic active contours for continuum robot detection using 3d ultrasound,” in IEEE International Conference on Robotics and Automation, ICRA ’12, 2012.

[5] X. Kang, H. Ren, J. Li, and W.-P. Yau, “Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions,” in Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on. IEEE, 2012, pp. 606–611.

[6] J. Cao, A. Tagliasacchi, M. Olson, H. Zhang, Z. Su, “Point Cloud Skeletons via Laplacian Based Contraction,” in Shape Modeling International Conference (SMI), pp. 21-23, 2010.