Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

M. Freiman; Y. Sela; Y. Edrei; O. Pappo; L. Joskowicz; R. Abramovitch

doi:10.1117/12.841242

9 March 2010 Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

M. Freiman, Y. Sela, Y. Edrei, O. Pappo, L. Joskowicz, R. Abramovitch

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76240S (2010) https://doi.org/10.1117/12.841242
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

M. Freiman, Y. Sela, Y. Edrei, O. Pappo, L. Joskowicz, and R. Abramovitch "Multi-class SVM model for fMRI-based classification and grading of liver fibrosis", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240S (9 March 2010); https://doi.org/10.1117/12.841242

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