TY - GEN
T1 - Classification of suspected liver metastases using fMRI images
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
AU - Freiman, Moti
AU - Edrei, Yifat
AU - Sela, Yehonatan
AU - Shmidmayer, Yitzchak
AU - Gross, Eitan
AU - Joskowicz, Leo
AU - Abramovitch, Rinat
PY - 2008
Y1 - 2008
N2 - This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.
AB - This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.
UR - http://www.scopus.com/inward/record.url?scp=79952404009&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85988-8_12
DO - 10.1007/978-3-540-85988-8_12
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C2 - 18979736
AN - SCOPUS:79952404009
SN - 354085987X
SN - 9783540859871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 100
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Y2 - 6 September 2008 through 10 September 2008
ER -
