Prediction of brain MR scans in longitudinal tumor follow-up studies

Lior Weizman; Liat Ben-Sira; Leo Joskowicz; Orna Aizenstein; Ben Shofty; Shlomi Constantini; Dafna Ben-Bashat

doi:10.1007/978-3-642-33418-4_23

Prediction of brain MR scans in longitudinal tumor follow-up studies

Lior Weizman, Liat Ben-Sira, Leo Joskowicz, Orna Aizenstein, Ben Shofty, Shlomi Constantini, Dafna Ben-Bashat

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

We present a new method for the estimation of the next brain MR scan in a longitudinal tumor follow-up study. Our method effectively incorporates information of the past scans in the time series to predict the future scan of the patient. Its advantages are that it requires no user intervention and does not assume any particular tumor growth model. Instead, the patient-specific tumor growth parameters are estimated individually from the past patient scans. To validate our method, we conducted an experimental study on four patients with Optic Path Gliomas (OPGs) and four patients with glioblastomas multiforma (GBM), each scanned at five time points. The tumor volumes in the predicted and actual future scans, both segmented by an expert radiologist, yield a mean volume overlap difference of 13.65% for the OPG patients, and 34.23% for the GBM patients.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
Editors	Georg Langs, Albert Montillo, Bjoern H. Menze, Antonio Criminisi, Zhuowen Tu, Le Lu, Georg Langs, Nicholas Ayache, Hervé Delingette, Bjoern H. Menze, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	179-187
Number of pages	9
ISBN (Print)	9783642334177
DOIs	https://doi.org/10.1007/978-3-642-33418-4_23
State	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 5 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	5/10/12 → 5/10/12

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-642-33418-4_23

Cite this

Weizman, L., Ben-Sira, L., Joskowicz, L., Aizenstein, O., Shofty, B., Constantini, S., & Ben-Bashat, D. (2012). Prediction of brain MR scans in longitudinal tumor follow-up studies. In G. Langs, A. Montillo, B. H. Menze, A. Criminisi, Z. Tu, L. Lu, G. Langs, N. Ayache, H. Delingette, B. H. Menze, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 179-187). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33418-4_23

Weizman, Lior ; Ben-Sira, Liat ; Joskowicz, Leo et al. / Prediction of brain MR scans in longitudinal tumor follow-up studies. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Georg Langs ; Albert Montillo ; Bjoern H. Menze ; Antonio Criminisi ; Zhuowen Tu ; Le Lu ; Georg Langs ; Nicholas Ayache ; Hervé Delingette ; Bjoern H. Menze ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a new method for the estimation of the next brain MR scan in a longitudinal tumor follow-up study. Our method effectively incorporates information of the past scans in the time series to predict the future scan of the patient. Its advantages are that it requires no user intervention and does not assume any particular tumor growth model. Instead, the patient-specific tumor growth parameters are estimated individually from the past patient scans. To validate our method, we conducted an experimental study on four patients with Optic Path Gliomas (OPGs) and four patients with glioblastomas multiforma (GBM), each scanned at five time points. The tumor volumes in the predicted and actual future scans, both segmented by an expert radiologist, yield a mean volume overlap difference of 13.65% for the OPG patients, and 34.23% for the GBM patients.",

author = "Lior Weizman and Liat Ben-Sira and Leo Joskowicz and Orna Aizenstein and Ben Shofty and Shlomi Constantini and Dafna Ben-Bashat",

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Weizman, L, Ben-Sira, L, Joskowicz, L, Aizenstein, O, Shofty, B, Constantini, S & Ben-Bashat, D 2012, Prediction of brain MR scans in longitudinal tumor follow-up studies. in G Langs, A Montillo, BH Menze, A Criminisi, Z Tu, L Lu, G Langs, N Ayache, H Delingette, BH Menze, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 179-187, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 5/10/12. https://doi.org/10.1007/978-3-642-33418-4_23

Prediction of brain MR scans in longitudinal tumor follow-up studies. / Weizman, Lior; Ben-Sira, Liat; Joskowicz, Leo et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Georg Langs; Albert Montillo; Bjoern H. Menze; Antonio Criminisi; Zhuowen Tu; Le Lu; Georg Langs; Nicholas Ayache; Hervé Delingette; Bjoern H. Menze; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ben-Sira, Liat

AU - Joskowicz, Leo

AU - Aizenstein, Orna

AU - Shofty, Ben

AU - Constantini, Shlomi

AU - Ben-Bashat, Dafna

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Weizman L, Ben-Sira L, Joskowicz L, Aizenstein O, Shofty B, Constantini S et al. Prediction of brain MR scans in longitudinal tumor follow-up studies. In Langs G, Montillo A, Menze BH, Criminisi A, Tu Z, Lu L, Langs G, Ayache N, Delingette H, Menze BH, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 179-187. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33418-4_23

Prediction of brain MR scans in longitudinal tumor follow-up studies

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