Automatic segmentation of breast carcinomas from DCE-MRI using a Statistical Learning Algorithm

Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumption...

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Bibliographic Details
Published in:2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012-05, Vol.2012, p.122-125
Main Authors: Jayender, J., Vosburgh, K. G., Gombos, E., Ashraf, A., Kontos, D., Gavenonis, S. C., Jolesz, F. A., Pohl, K.
Format: Article
Language:English
Subjects:
Breast
Clustering algorithms
Hidden Markov models
Image segmentation
Magnetic resonance imaging
Mathematical model
Tumors
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Summary:Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) for automatically segmenting cancer from a region selected by the user on DCE-MRI. In this preliminary study, SLATS appears to demonstrate high accuracy (78%) and sensitivity (100%) in segmenting cancers from DCE-MRI when compared to segmentations performed by an expert radiologist. This may be a useful tool for delineating tumors for image-guided interventions.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2012.6235499