Liver metastasis early detection using fMRI based statistical model

Moti Freiman*, Yifat Edrei, Eitan Gross, Leo Joskowicz, Rinat Abramovitch

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

We present a novel method for computer aided early detection of liver metastases. The method used fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics were evaluated from T2*-W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model was built to differentiate between metastatic and healthy liver tissue. The model was constructed from 128 validated fMRI samples of metastatic and healthy mice liver tissue using histogram-based features and SVM classification engine. The model was subsequently tested with a set of 32 early, non-validated fMRI samples. Our model yielded an accuracy of 84.38% with 80% precision.

Original languageAmerican English
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages584-587
Number of pages4
DOIs
StatePublished - 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: 14 May 200817 May 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Country/TerritoryFrance
CityParis
Period14/05/0817/05/08

Keywords

  • Computer-aided diagnosis
  • Early detection
  • Liver metastasis
  • Statistical analysis
  • fMRI analysis

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