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Proceedings Paper

Multi-class SVM model for fMRI-based classification and grading of liver fibrosis
Author(s): M. Freiman; Y. Sela; Y. Edrei; O. Pappo; L. Joskowicz; R. Abramovitch
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Paper Abstract

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240S (9 March 2010); doi: 10.1117/12.841242
Show Author Affiliations
M. Freiman, The Hebrew Univ. of Jerusalem (Israel)
Y. Sela, The Hebrew Univ. of Jerusalem (Israel)
Y. Edrei, Hadassah Hebrew Univ. Medical Ctr. (Israel)
O. Pappo, Hadassah Hebrew Univ. Medical Ctr. (Israel)
L. Joskowicz, The Hebrew Univ. of Jerusalem (Israel)
R. Abramovitch, Hadassah Hebrew Univ. Medical Ctr. (Israel)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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