New developments and improvements in Deformable Image ... · Shape based regularization ... “The...

4D Treatment Planning Workshop 2015 - 27.11.2015 - Oncoray - Dresden, Germany - Hugo Furtado

Furtado H.1,3, Renner A.2,3, Daniel M. 2,3, Buschmann M. 2,3,

Seppenwoolde Y.2,3 , Georg D.2,3, Birkfellner W.1,3

1 Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Austria 2 Department of Radiation Oncology, Medical University Vienna, Austria

3 Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University Vienna, Austria

New developments and improvements in Deformable Image Registration

4D Treatment Planning Workshop 2015 - 27.11.2015 - Oncoray - Dresden, Germany - Hugo Furtado 2

Outline

• Introduction

Motivation and objectives

• Methodology

Common framework

Examples

• Ongoing projects at the MUW

Autocontouring

Featurelets

• New trends / Ideas

• Conclusion


Motivation - Why the strong need for DIR

Patient anatomy

changes over time

Pla

nn

ing

Treatment

Bladder

Uterus


Motivation - Why the strong need for DIR

• With deformable image registration we can

Dose accumulation

Auto contouring (atlas based)

Contour propagation (Planning CT to weekly CT)

Multi modal image manual contour guidance


Motivation - Typical challenges to solve

• Different image content between modalities

• Very big deformations

• Appearing / Disappearing tissue

• Validation! (very important, see next talk)


Outline

• Introduction


• Methodology

Common framework

Examples


Autocontouring

Featurelets


• Conclusion


How does it work - basic framework

A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Transactions on Medical Imaging 32(7), pp. 1153–1190, 2013.


How does it work



How does it work



How does it work



How does it work



How does it work



How does it work



Simple example

B-spline + Image based similarity


Simple example

B-spline + Image based similarity


Making it more complex

• Clinical research version software from commercial vendors

• DIR software containing some of the latest improvements

• Combined approach between image and model based

Image similarity measure

Grid regularization

Shape based regularization

Controlling structures are used

O. Weistrand and S. Svensson, “The anaconda algorithm for deformable image registration in radiotherapy,” Medical Physics 42(1), pp. 40–53, 2015.


Making it more complex - objective function

𝒇 𝒗 = 𝜶𝑪 𝒗 + 𝜷𝑯 𝒗 + 𝜸𝑺 𝒗 + 𝜹𝑫(𝒗)

Image similarity

(correlation coefficient)

Regularization of DF

Controlling structures

Smoothness

Invertibility

Penalty for

„non-intuitive“

deformations

ROI POI



Regularization of the deformation field

Shackleford et. al., “Analytic regularization of uniform cubic b-spline deformation fields.,” Medical image computing and computer-assisted intervention : MICCAI ... International

Conference on Medical Image Computing and Computer-Assisted Intervention 15(Pt 2), pp. 122–129, 2012.

Johansson et. al., "Globally optimal displacement fields using local tensor metric," in Image Processing (ICIP), 2012 19th IEEE International Conference on , vol., no.,

pp.2957-2960, Sept. 30 2012-Oct. 3 2012


Regularization of the deformation field

Penalize „non-intuitive deformations“



Guiding structures



Outline

• Introduction


• Methodology

Common framework

Examples


Autocontouring

Featurelets


• Conclusion


Autocontouring for cervical cancer patients

• We used as atlas to contour CTs or CBCTs:

Planning CT

One weekly CT scan with comparable bladder volume

One weekly CT scans with comparable uterus position

Several atlases “stapled” together (using several comparable structures)

Same with pre delineation to guide DIR


Autocontouring - Results

• Atlases with comparable bladder volume work better

• Effect less pronounced for the target

Figure B. Auto-contouring using the Dice Similarity Index (DSI) showing that using only the planning

CT as atlas did not result in convincing contour quality for the bladder and the target volume. Using a

scan with a comparable bladder volume the results improved.

(Data (x-axis) in chronological order).


Autocontouring - Results

• Always bellow the intra-observer variability

• Both evaluated methods quite similar


Contours in real life


Contours in real life


Head and Neck

CT week 0 CT week 4 - DIR


Head and Neck

CT week 0 CT week 4


Conclusion

• Results are reasonable…

• But still there are a lot of issues

Structures need correcting

DICE scores are relatively low

• Method is more elaborate and leads theoretically to better DFs but…

Cannot control optimization parameters

Cannot assess the influence of all the parts of the objective function

Method still a “grey” box…


New developments - Featurelets

• As with other methods

Model independent - purely intensity based

The original idea contains minimization of elastic energy, though

• Piecewise rigid registration method

Image is divided in sub-regions (featurelets)

Each featurelet is registered on the target image

Deformation field constructed from the displacements



Söhn et. al., “Model-independent, multimodality deformable image registration by local matching of anatomical features and minimization of elastic energy,”

Medical Physics 35(3), pp. 866–878, 2008.




Featurelets - Previous results

• Applied on 2 phantoms, lung and pelvic cases

• Comparison with commercial software

Fabri et. al., “A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy,” Zeitschrift fur Medizinische Physik 23(4), pp. 279–290, 2013.

Zambrano et. al., “Performance validation of deformable image registration in the pelvic region,” Journal of Radiation Research 54(SUPPL.1), pp. i120–i128, 2013.


Featurelets - Influence of different featurelet size

103 voxel 153 voxel 353 voxel


Featurelets - Conclusion

• Simple method

• Results are in line with similar approaches

• Future plans:

GPU implementation

DF regularization

Fixing structures

Etc...


Outline

• Introduction


• Methodology

Common framework

Examples


Autocontouring

Featurelets


• Conclusion


New trends / Ideas

• Initial Guess

Manual: how to do it?

Atlas based - find the image that most resembles your target

With automatic anatomical landmark annotation

Werner et. al., “Assessing accuracy of non-linear registration in 4d image data using automatically detected landmark correspondences,” Progress in Biomedical

Optics and Imaging - Proceedings of SPIE 8669, 2013.


New trends / Ideas

Regional constraints: bone should not deform or should only piecewise deform

König et. al. OC-0409: Deformable image registration with guaranteed local rigidity, Radiotherapy and Oncology, Volume 115 , S197 - S198


New trends / Ideas

Regional constraints: sliding organs or regions

Berendsen et. al., “Registration of organs with sliding interfaces and changing topologies,” Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9034, 2014.


New trends / Ideas

• Modeling the way humans deal with “invisible” organ boundaries

We make a lot of assumptions based on prior knowledge and we “interpolate”


New trends / Ideas

“Regmentation”: joint optimization of segmentation and registration metrics

Blaffert et. al., “Bladder segmentation in mr images with watershed segmentation and graph cut algorithm,” Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9034, 2014

𝒇 𝒗 = 𝜶𝑪 𝒗 + 𝜷𝑯 𝒗 + 𝜸𝑺 𝒗 + 𝜹𝑫(𝒗)

Controlling structures

ROI


Discussion / Conclusions

• Strong need for DIR

• Many new improvements

• Still a bit disappointing…

• Many things to do in the future


Thank you for your attention! Funding from the Austrian Federal Ministry of Science, Research

and Economy and the National Foundation for Research,

Technology and Development is greatly acknowledged.

http://www.meduniwien.ac.at/hp/radonc/

Acknowledgements

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New developments and improvements in Deformable Image ... · Shape based regularization ... “The...

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