Our group have been developing methods for MRI-alone prostate cancer radiation therapy treatment planning. To assist with clinical validation of the workflow we are investigating a cloud platform solution for research purposes. Benefits of cloud computing can include increased scalability, performance and extensibility while reducing total cost of ownership. In this paper we demonstrate the generation of DICOM-RT directories containing an automatic average atlas based electron density image and fast pelvic organ contouring from whole pelvis MR scans

Burdett, N.

Chandra, S.

Dowling, J. A.

Fripp, J.

Ghose, S.

Greer, P. B.

Parker, J.

Pichler, P.

Rivest-Hénault, D.

Salvado, O.

Stanwell, P.

Sun, J.

Journal of Physics : Conference Series

Rivest-Henault, D.

University of Queensland eSpace

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Automatic Atlas Based Electron Density and Structure Contouring for MRI-based Prostate
Radiation Therapy on the Cloud
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2014 J. Phys.: Conf. Ser. 489 012048
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Automatic Atlas Based Electron Density and Structure 
Contouring for MRI-based Prostate Radiation Therapy on the 
Cloud 
J.A. Dowling*
1
, N. Burdett
1
, P.B. Greer
2,3
, J. Sun
2,3
, J. Parker
2
, P. Pichler
2
, P. 
Stanwell
2,3
, S. Chandra
1
, D. Rivest-Hénault
1
, S. Ghose
1
, O. Salvado
1
 and J. Fripp
1
 
1
CSIRO Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, 
Queensland, Australia  
2
Calvary Mater Newcastle Hospital, New South Wales, Australia  
3
University of Newcastle, New South Wales, Australia  
 
 
E-mail: jason.dowling@csiro.au 
 
Abstract. Our group have been developing methods for MRI-alone prostate cancer radiation 
therapy treatment planning.  To assist with clinical validation of the workflow we are 
investigating a cloud platform solution for research purposes.  Benefits of cloud computing can 
include increased scalability, performance and extensibility while reducing total cost of 
ownership.  In this paper we demonstrate the generation of DICOM-RT directories containing 
an automatic average atlas based electron density image and fast pelvic organ contouring from 
whole pelvis MR scans.       
1. Introduction 
 
There are a number of benefits of the use of MRI for soft tissue visualisation in prostate cancer 
radiation therapy.  However treatment planning requires the use of computed tomography (CT) scans 
for simulation.  This is necessary as there are currently limited methods to convert MRI intensities to 
electron density (which is a requirement for dose calculations to be performed directly on MRI scans).  
Advantages of planning directly on MRI scans would include a reduction in costs and the uncertainties 
introduced by the registration process. A further motivation is the recent developments in MRI 
simulation and MRI accelerators (1–3) which bring the potential for MRI based adaptive treatment.   
 
Our research group has been investigating the feasibility of MRI-alone treatment planning for prostate 
cancer radiation therapy.  We have previously published methods for electron density generation from 
MRI with <2% difference between pseudo-CT and planning CT in point dose at isocentre (4), fast 
automatic organ contouring (5,6) and proposed a revised MRI-alone workflow (7).  To assist with 
clinical validation of the MR-alone workflow this paper concentrates on a remote cloud platform 
solution.   
 
                                                     
*  To whom any correspondence should be addressed. 
XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution
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2. Method 
2.1.1. Image data 
 
As described in Dowling, et al. (4), a whole pelvis MRI atlas was generated based on 39 manually 
delineated 1.5T T2w MRI scans from patients prior to external beam radiation therapy for localised 
prostate cancer.  In addition a conjugate electron-density atlas was generated from the co-registered 
CT-MRI scans (Figure 1).  These atlases contain contours for the pelvic bones, bladder, rectum and 
prostate. 
 
 
 
 
 
Figure 1. MRI (top) and matching pseudo CT (bottom) pelvic atlas with surface models of the 
main pelvic organs (bones, bladder, rectum and prostate).  
 
 
XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
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2.1.2. Processing software 
 
The current cloud pipeline for pseudo CT generation and automatic contouring is shown in Figure 2. 
The interface requires a DICOM series containing a whole pelvis (male) large field of view T2w MRI.   
Once received the DICOM series is converted into a single 3D volume (NifTI file format, 
http://nifti.nimh.nih.gov/nifti-1 ) and is pre-processed prior to average atlas based electron density 
estimation and initial organ contouring.  Atlas based segmentation usually involves an atlas image 
(generally an average of a set of images) with a matching set of organ labels (eg. Figure 1).  To 
segment a new image, the MRI atlas is registered to a patient’s whole pelvis T2w MR image to obtain 
a good correspondence between structurally equivalent regions in the two images, and then the labels 
defined on the atlas are propagated to the image.  
 
 
Figure 2. Overview of the main steps involved in average atlas based pseudo-CT and structure 
generation on the cloud  
 
XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
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The technical details of the atlas generation has been reported previously (7).  To generate a pseudo-
CT from a patient’s MRI the same transformation and deformations are then applied to the CT atlas 
(Figure 1, right) to create a pseudo-CT corresponding to the patient MRI scan anatomy. This pseudo-
CT is then used for dose planning and digitally reconstructed radiograph (DRR) generation (Figure 2).   
In the final step, the resulting volume and structures are converted to a DICOM-RT series for the 
clinical site to download, QA and modify if necessary. 
 
2.1.3. Cloud interface and environment 
 
A medical imaging analysis and visualization platform has been developed at the CSIRO Australian e-
Health Research Centre called MILXView, incorporating a number of advanced algorithms that 
provide improved automated image analysis and interpretation of medical imaging data. MILXView is 
designed to process data on a desktop install however the aim is to enable access and execute these 
algorithms in a cloud environment.  The cloud offers several distinct advantages over desktop 
processing; including achieving economies of scale, reduced spending of technology infrastructure and 
support, improved accessibility, minimising licensing new software and the ability to deploy software 
upgrades instantly. 
 
Galaxy (8) has been adapted to execute MILXView binaries in the cloud. Galaxy is a collection of 
XML and Python scripts that allow users to upload data to a cloud environment and then execute 
binaries using the uploaded data as arguments. Tools can be quickly added to the user interface by 
updating a single configuration XML file which states the location of a tool XML file.  This tool XML 
file specifies the binaries or scripts to be executed and their arguments and output parameters. 
 
All data uploaded and produced via Galaxy is stored in a database. Galaxy uses SQLite by default, but 
can use MySQL or PostgreSQL to overcome concurrency limitations. Galaxy contains a standalone 
web server. However, some tasks such as serving static content can be offloaded to a dedicated server 
for efficiency.  
 
 
 
 
Figure 3. Screen snapshot from the proposed cloud interface. Uploaded DICOM series and 
automatically generated DICOM-RT directories are maintained in the history panel on the right hand 
side.     
 
The installation of Galaxy is simple and nearly identical for UNIX/Linux and Mac OS X 
environments. It is not supported under Windows so users would have to build their own Python Eggs 
(a method of distributing Python packages). Galaxy can be run on a single desktop, or on a compute 
XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
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cluster. Galaxy currently supports TORQUE, PBS, PBS Pro, Platform LSF, HT Condor, Open grid 
Engine and Sun Grid Engine clusters and does not require a dedicated or special cluster configuration.   
Finally, Galaxy can be instantiated on cloud computing infrastructures (primarily Amazon Elastic 
Compute Cloud (EC2)), or in a user created cloud environment using CloudMan. 
 
3. Results  
 
3.1.1. Pseudo-CT generation 
 
The cloud implementation enables the generation of pseudoCT and automatic bone, bladder, prostate, 
and rectum contours within approximately 3.5 minutes (using the average atlas shown in Figure 1).  
Figure 4 shows qualitative results from the automatic conversion of a 3T T2w whole pelvis MRI.        
  
 
 
 
 
Figure 4. Left: Axial sample slice from a whole pelvis T2w MR scan (not part of the atlas set).  The 
central image shows the result from deformable registration of the whole pelvis MRI atlas (from 
Figure 1) to the new scan; and the matching pseudo-CT is shown on the right.  Automatic organ 
segmentations for the prostate, bladder, rectum and bones have been overlaid. 
 
 
4. Discussion 
 
The cloud research in this paper has been motivated by a need to allow increased scalability, 
performance and extensibility while reducing total cost of ownership and providing access to powerful 
CPU and GPU clusters for efficient parallel methods in medical image analysis.  The cloud 
architecture has been applied in this work to demonstrate a previously published method working 
through a cloud interface and producing DICOM-RT files which are ready for import into a 
commercial treatment planning system for research purposes.  The architecture allows us to easily 
include new methods for pseudo-CT and automatic contouring (for example fast, parallel multi-atlas 
registration and selection methods), and to interface with both internal and commercial clusters.       
 
 
 
 
XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
5
  
 
 
 
 
Acknowledgments 
Jason Dowling acknowledges funding support from the Cancer Council NSW (RG 11-05), Prostate 
Cancer Foundation of Australia (YI2011), Movember and Cure Cancer Australia. 
 
 
References 
1.  Karlsson M, Karlsson MG, Nyholm T, Amies C, Zackrisson B. Dedicated magnetic resonance 
imaging in the radiotherapy clinic. International Journal of Radiation Oncology, Biology, 
Physics. Elsevier; 2009 Jun;74(2):644–51.  
2.  Raaymakers BW, Lagendijk JJW, Overweg J, Kok JGM, Raaijmakers AJE, Kerkhof EM, et al. 
Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept. Physics in 
Medicine and Biology. 2009;54(12):N229–N237.  
3.  Lee YK, Bollet M, Charles-Edwards G, Flower MA, Leach MO, McNair H, et al. Radiotherapy 
treatment planning of prostate cancer using magnetic resonance imaging alone. Radiotherapy 
and Oncology. 2003 Feb;66(2):203–16.  
4.  Dowling JA, Lambert J, Parker J, Salvado O, Fripp J, Capp A, et al. An atlas-based electron 
density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and 
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5.  Dowling JA, Fripp J, Chandra S, Pluim JPW, Lambert J, Parker J, et al. Fast automatic multi-
atlas segmentation of the prostate from 3D MR images. Prostate Cancer Imaging. Image 
Analysis and Image-Guided Interventions. Springer; 2011. p. 10–21.  
6.  Chandra S, Dowling J, Shen K, Raniga P, Pluim J, Greer P, et al. Patient Specific Prostate 
Segmentation in 3D Magnetic Resonance Images. IEEE Transactions on Medical Imaging. 
2012 Aug 2;31.  
7.  Greer PB, Dowling JA, Lambert JA, Fripp J, Parker J, Denham JW, et al. A magnetic 
resonance imaging-based workflow for planning radiation therapy for prostate cancer. The 
Medical Journal of Australia. 2011 Feb;194(4):S24–S27.  
8.  Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting 
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XVII International Conference on the Use of Computers in Radiation Therapy (ICCR 2013) IOP Publishing
Journal of Physics: Conference Series 489 (2014) 012048 doi:10.1088/1742-6596/489/1/012048
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Automatic Atlas Based Electron Density and Structure Contouring for MRI-based Prostate Radiation Therapy on the Cloud

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Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud

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Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud

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