Quantifying evolving processes in multimodal 3D medical images

Yuhang Wang, Tilmann Steinberg, Fillia Makedon, James Ford, Heather Wishart, Andrew Saykin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Quantitative measurements of changes in evolving brain pathology, such as multiple sclerosis lesions and brain tumors, are important for clinicians to perform pertinent diagnoses and to help in patient follow-up. Lesions or tumors can vary over time in size, shape, location and composition because of natural pathological processes or the effect of a drug treatment or therapy. In the past, people have used as a quantitative measurement the change in total or regional lesion/tumor volume. In this paper we propose a new model to quantify changes in evolving processes in multimodal 3D medical images. We believe this model reflects changes in pathology more accurately because it simultaneously takes into account information in multiple imaging modalities and the location of lesion/tumor voxels. We demonstrate the effectiveness of this model with experiments on synthetic lesion data.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
EditorsR.E. Ellis, T.M. Peters
Pages101-108
Number of pages8
Volume2879
EditionPART 2
StatePublished - 2003
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings - Montreal, Que., Canada
Duration: Nov 15 2003Nov 18 2003

Other

OtherMedical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings
CountryCanada
CityMontreal, Que.
Period11/15/0311/18/03

Fingerprint

Tumors
Drug therapy
Pathology
Brain
Imaging techniques
Chemical analysis
Experiments

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Engineering(all)

Cite this

Wang, Y., Steinberg, T., Makedon, F., Ford, J., Wishart, H., & Saykin, A. (2003). Quantifying evolving processes in multimodal 3D medical images. In R. E. Ellis, & T. M. Peters (Eds.), Lecture Notes in Computer Science (PART 2 ed., Vol. 2879, pp. 101-108)

Quantifying evolving processes in multimodal 3D medical images. / Wang, Yuhang; Steinberg, Tilmann; Makedon, Fillia; Ford, James; Wishart, Heather; Saykin, Andrew.

Lecture Notes in Computer Science. ed. / R.E. Ellis; T.M. Peters. Vol. 2879 PART 2. ed. 2003. p. 101-108.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Y, Steinberg, T, Makedon, F, Ford, J, Wishart, H & Saykin, A 2003, Quantifying evolving processes in multimodal 3D medical images. in RE Ellis & TM Peters (eds), Lecture Notes in Computer Science. PART 2 edn, vol. 2879, pp. 101-108, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings, Montreal, Que., Canada, 11/15/03.
Wang Y, Steinberg T, Makedon F, Ford J, Wishart H, Saykin A. Quantifying evolving processes in multimodal 3D medical images. In Ellis RE, Peters TM, editors, Lecture Notes in Computer Science. PART 2 ed. Vol. 2879. 2003. p. 101-108
Wang, Yuhang ; Steinberg, Tilmann ; Makedon, Fillia ; Ford, James ; Wishart, Heather ; Saykin, Andrew. / Quantifying evolving processes in multimodal 3D medical images. Lecture Notes in Computer Science. editor / R.E. Ellis ; T.M. Peters. Vol. 2879 PART 2. ed. 2003. pp. 101-108
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