Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm

J Jayender; K G Vosburgh; E Gombos; A Ashraf; D Kontos; S C Gavenonis; F A Jolesz; K Pohl

doi:10.1109/ISBI.2012.6235499

Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm

Proc IEEE Int Symp Biomed Imaging. 2012 May:2012:122-125. doi: 10.1109/ISBI.2012.6235499. Epub 2012 Jul 12.

Authors

J Jayender¹, K G Vosburgh¹, E Gombos¹, A Ashraf², D Kontos², S C Gavenonis², F A Jolesz¹, K Pohl²

Affiliations

¹ Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
² Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104.

Abstract

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.

Abstract

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