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

Author

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

Author_Institution

Med. Sch., Dept. of Radiol., Harvard Univ., Boston, MA, USA

fYear

2012

fDate

2-5 May 2012

Firstpage

122

Lastpage

125

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.

Keywords

biomedical MRI; cancer; image segmentation; learning (artificial intelligence); mammography; medical image processing; statistical analysis; tumours; DCE-MRI; angiogenic activity; automatic segmentation; breast carcinoma; malignant tumors; statistical learning algorithm; tumor segmentation; Breast; Clustering algorithms; Hidden Markov models; Image segmentation; Magnetic resonance imaging; Mathematical model; Tumors;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on

Conference_Location

Barcelona

ISSN

1945-7928

Print_ISBN

978-1-4577-1857-1

Type

conf

DOI

10.1109/ISBI.2012.6235499

Filename

6235499