15 February Partial volume correction for liver metastases and lymph nodes1Institute for Medical...

15 FebruaryPartial volume correction for liver metastases and lymph nodes

1Institute for Medical Image Computing /16SPIE 2010

Partial volume correction for volume estimation

of liver metastases and lymph nodes in CT scans

using spatial subdivisionFrank Heckel1, Volker Dicken1, Tilman Bostel2,

Michael Fabel3, Andreas Kießling4, Heinz-Otto Peitgen1

1 Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany2 Johannes Gutenberg University, Clinic and Out-patients’ Clinic for Diagnostic and Interventional Radiology, Mainz, Germany3 Christian-Albrechts-University, Department of Diagnostic Radiology, Kiel, Germany4 Philipps-University, Department of Diagnostic Radiology, Marburg, Germany

Overview

› Motivation

› Basic Idea

› Algorithm

› Evaluation

› Open Problems

› Conclusion

› Outlook

Motivation

› Clinical application: Oncological therapy monitoring» Assessment of tumor growth from consecutive CT scans» RECIST 1.11: Sum of maximum diameters (clinical standard)

» Volume is more reliable2

- Unfortunately: Progress / Response clinically not yet defined- Segmentation needed

+ 20% Progressive disease

- 30% Partial response

1 Eisenhauer, E., Therasse, P., Bogaerts, J., Schwartz, L., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., Rubinstein, L., Shankar, L., Dodd, L., Kaplan, R., Lacombe, D., and Verweij, J.,“New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1),” European journal of cancer 45, 228–247 (2009)

2 Bornemann, L., Dicken, V., Kuhnigk, J.-M., Wormanns, D., Shin, H.-O., Bauknecht, H.-C., Diehl, V., Fabel, M., Meier, S., Kress, O., Krass, S., and Peitgen, H.-O., “OncoTREAT: a software assistant for cancer therapy monitoring,” International Journal of Computer Assisted Radiology and Surgery 1(5), 231–242 (2007)

Motivation

› Border of tumor not clear

Partial volume effect:One voxel represents two or more

tissues because of limited spatial resolution of

Liver tissue

Tumor + Liver tissue(partial-volume-voxels)

Motivation

› Border of tumor not clear Threshold for segmentation not clear» Different segmentations by different readers / in different scans» Significant difference in volume

56.61ml (-20%) 70.8ml 86.46ml (+22.1%)

eroded initial dilated

Basic Idea

› Weight each partial-volume-voxels based on its value and the values of its influencing tissues and calculate volume by the weighted sum of all voxels

› Challenge: Typically different types of tissue outside the lesion

› Assumptions: » Lesion is ellipsoidal and compact» Partial volume voxels are a

mixture of 2 tissues

Consider partial volume effectwhen calculating the tumor’s volume

Algorithm

› Definition of 5 parts» Calculated by successive erosion / dilation

› Spatial subdivision of the lesion into 3D equiangular parts» To cover different tissues (inside and outside of the lesion)

Segmentation

Inner partial volume area

Outer partial volume area

Inner tissue area

Outer tissue area

Lesion core

Algorithm

› Calculate weight w for each voxel

› Core and inner tissue w = 1

› Outer tissue w = 0

› For each segment of the subdivision» Calculate the weight w of each partial volume

voxel as a linear combination of:- The value of the partial volume voxel- The average outer tissue value- The average inner tissue value

» w is clamped to [0,1]

Algorithm

› Volume is given by weighted sum of the volume of each voxel in partial volume areas, tissue areas and lesion core

› Calculation time: 2s

› Result:

64.57ml (-12.8%) 74.06ml 83.18ml (+12.3%)

0.056.61ml (-20%) 70.8ml 86.46ml (+22.1%)voxel-

count:corrected:

eroded initial dilated

Algorithm

› Special cases: » Average outer partial volume value similar to average outer tissue

value w = 0 (assumption: intended by user)

» Lesion too small- Not enough voxels in inner tissue- Use average lesion core value instead

» Inner and outer tissue do not represent tissues of partial volume voxels (w << 0 or w >> 1)

- Use distance to inner tissue instead

Voxel-Count

Evaluation

› Phantom:» 31 lesions (liver metastases, lymph nodes)» More accurate estimation of the volume:

Average difference

to real volume in %

Standard deviation in %

Voxel-CountCorrected

readers

Evaluation

› Multi-reader:» 132 liver metastases (no rim-enhancing), 2 readers» Significant reduction of inter-observer variability:

3.68ml 4.29ml

4.03ml 4.08ml

= +16.9%= +1.12%

voxel-count:correcte

Reader 2Reader 1

Open Problems

› Rim-enhancing lesions» Rim is not always correctly covered

by the inner tissue area

› Separated “islands” might begenerated» Because only a voxel’s value is used

for calculation, not its position

› Subdivision segments might includedifferent tissue classes

› Calculated volume is inconsistentwith the visible segmentation result

Conclusion

› Algorithm:» Considers different tissues around a lesion» Fast» Not restricted to liver metastases and lymph nodes

› Result of evaluations:» More accurate volume estimation» Significant reduction of inter-observer-variability

More robust, reliable and reproducible volume quantificationeven for complex lesions

Outlook

› Investigate 5mm phantom results

› Improve subdivision so each segment covers exactly one tissue-class

› Adaptive calculation of the size of partial volume and the tissue areas, to cover rim-enhancing lesions correctly

› Solve “island” issue

› Further evaluations

Thanks for your attention!Any Questions?

frank.heckel@mevis.fraunhofer.de

Thanks to:

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