Enhancement Techniques for Human Anatomy Visualization
Hirofumi Seo∗

Takeo Igarashi†

The University of Tokyo
Tokyo, Japan
seo.hirofumi@mail.u-tokyo.ac.jp(a)

The University of Tokyo
Tokyo, Japantakeo@acm.org

(b)

(c)

(e)

(d)

(f)

Figure 1: (a) Original rendering result. (b) Screen Space Ambient Occlusion, Depth of Field, and Depth Cueing have been addedto (a). (c) Original polygonal tubes. (d) (c) with “caps" seems to thick and hollow tubes. (e),(f) Wireframe of (c),(d) respectively.

ABSTRACT1We propose two simple and efficient visualization techniques forassisting understanding of complex three-dimensional structures
like human anatomy: (1) applying Screen Space Ambient Occlusion(SSAO), Depth of Field (DoF), and Depth Cueing (DC) to an original
rendering result image in real-time, and (2) adding “caps" to thinpolygonal tube structures which results in pseudo-thick, hollow
structures with a small amount of additional polygons.

3D reconstruction of human anatomy from medical images like CTor MRI is quite common, but the visualization method is often still
very old. We introduce two simple techniques to enhance visualization of human anatomy: one is post processing for rendering, andanother is a simple trick for geometry processing.

CCS CONCEPTS• Computing methodologies → Visibility;

KEYWORDS
Visualization, Medical, Anatomy, Blood Vessel
ACM Reference Format:
Hirofumi Seo and Takeo Igarashi. 2018. Enhancement Techniques for Human Anatomy Visualization. In Proceedings of SA ’18 Posters. ACM, New
York, NY, USA, 2 pages. https://doi.org/10.1145/3283289.3283332∗ Department of Creative Informatics, Project Academic Support Specialist, M.D.
† Department of Creative Informatics, ProfessorPermission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
SA ’18 Posters, December 04-07, 2018, Tokyo, Japan© 2018 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-6063-0/18/12.
https://doi.org/10.1145/3283289.3283332

INTRODUCTION

2 METHOD
2.1 Applying Post Processing in Real-Time
Human anatomy is so complex that just visualizing it in traditional
ways is insufficient for easy understanding like Figure 1(a).
Three post process effects - SSAO [Aalund and Bærentzen 2013],
DoF [Demers 2004] and DC - are all available in real-time, at least
with some built-in game engines. Each effect uses screen spacenormal vectors and / or depth values, so compatible with deferredshading, but also applicable in forward shading or volume rendering if the vectors and values are stored. SSAO emphasizes occluded,
sheltered areas, while DoF and DC emphasize depth of visible objects. So the combination of the effects is helpful for understanding
complicating 3D structures like human anatomy. See the results onFigure 1 (a), (b), and the step by step outputs on Figure 2.

2.2

Adding “Caps" to Thin Polygonal Tubes

There are a lot of tube-like branching structures inside our body,
blood vessels and bronchi for example. One of the methods forrepresenting such structures is extracting the central lines andradii at each vertex on the lines, and making polygonal tubes byextruding a base circle along the lines, like Figure 3(a)-(c). However,
the generated tubes look strange because they have no thickness

SA ’18 Posters, December 04-07, 2018, Tokyo, Japan(a) Original(b) SSAO

H. Seo et al.

(c) (a) + SSAO(d) (a) + DoF(e) DC(f) (a) + DC(g) (a) + SSAO+ DoF+ DC

Figure 2: 3D reconstruction of a thoracic CT volume image using GPU-based marching cubes [Dyken and Ziegler 2010;
Lorensen and Cline 1987].
(a)(b)(c)

(c)

(d)

(a)

(b)

(d)

Figure 3: A polygonal tube generation by it’s central lines
and radii at each vertex.
like Figure 1(c). So polygons for thickness and inner walls like Figure
3(d) is often added, but this method causes a lot of new polygons
especially when the central lines have many vertices despite mostof the inner walls are not visible in many cases.
Our method adds “pseudo inner wall" polygons instead of innerwall polygons like Figure 4(b). The “thickness" polygons and the“pseudo inner wall" polygons result in a “cap" on the tube. Thevertices on the border of outer walls, thickness, and pseudo innerwalls have been separated. Each vertex has a different normal vector
like Figure 4(c). Reversing the normal vectors of thickness polygons
like the green vectors on Figure 4(c) makes the cross section look
like inner parts if we apply two-sided material to the tube.
Normal vectors of the pseudo inner wall polygons are the mostimportant trick of our method. The inner direction vector perpendicular to the cap has been applied as a normal vector at the central
tip vertex, and the direction vector aiming from the central tip
to each border vertex at each vertex like Figure 4. This mimics areversed dented hemisphere, and the shaded result looks like as
if the tube has inner walls like Figure 4(d) and 1(d). This method
not only saves the number of polygons to add in comparison tomaking actual inner walls, but also contributes to hide the penetration of objects into the tubes. See the white arrow at Figure 1(c)and (d). This technique has been used in our real-time virtual brain
aneurysm clipping surgery software project [Seo et al. 2018].

3RESULTWe have tested both methods on Microsoft® Windows® 10 PC
with CPU Intel® Core™ i7-7700K 4.2GHz, Memory 64G, and GPU
NVIDIA® GeForce® GTX 1080Ti. We have implemented GPU-basedmarching cubes method based on [Dyken and Ziegler 2010] andintegrated it into Epic Games Unreal Engine 4.16. The volume image

Figure 4: (a) Original polygonal tubes. (b) Add “cap" to (a).
(c) Normal vectors on each vertex. (d) The shaded result. Seealso Figure 1(c) and (d).
used consists of 256 x 256 x 463 voxels, and the generated object
shown in Figure 1 and 2 has about 1.4 millions triangle polygons.
The frame per second (FPS) of Figure 1 and 2 was about 63 FPS
without any post process effect, while 60 FPS with all SSAO, DoF,and DC effects. We think this is fast enough. Figure 1(c) and (d) are
the rendered result of Figure 4(a) and (d) by Unreal Engine 4.16.

ACKNOWLEDGMENTS
This research was supported by AMED under Grant Number
JP18he1602001. The authors would like to thank Dr. Taichi Kin, M.D.
and Dr. Naoyuki Shono, M.D. of The Department of Neurosurgery
at The University of Tokyo.

REFERENCES
Frederik P. Aalund and J. Andreas Bærentzen. 2013. A Comparative Study of ScreenSpace Ambient Occlusion Methods (Bachelor Thesis).
Joe Demers. 2004. Depth of Field: A Survey of Techniques. GPU Gems, Vol. Chapter
23. http://developer.download.nvidia.com/books/HTML/gpugems/gpugems_ch23.
html
Christopher Dyken and Gernot Ziegler. 2010. GPU-accelerated data expansion forthe marching cubes algorithm. Proc. PGU Technology Conf., 115–123. http://www.
nvidia.com/content/gtc-2010/pdfs/gtc10_session2020_hp5mc_dyken_ziegler.pdf
William E. Lorensen and Harvey E. Cline. 1987. Marching Cubes: A High Resolution
3D Surface Construction Algorithm. In Proceedings of the 14th Annual Conference
on Computer Graphics and Interactive Techniques (SIGGRAPH ’87). ACM, New York,NY, USA, 163–169. https://doi.org/10.1145/37401.37422
Hirofumi Seo, Naoyuki Shono, Taichi Kin, and Takeo Igarashi. 2018. Real-Time Virtual
Brain Aneurysm Clipping Surgery. In Proceedings of the 24th ACM Symposium on
Virtual Reality Software and Technology. ACM.