Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

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

Institution:
1Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
3Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Publication Date:
Jun-2012
Journal:
Int Symp Biomed Imaging (ISBI) 2012
Volume Number:
2012
Pages:
122-5
Citation:
Proc IEEE Int Symp Biomed Imaging. 2012 May; 2012: 122-5.
PubMed ID:
28603582
PMCID:
PMC5464330
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
Generated Citation:
Jayender J., Vosburgh K.G., Gombos E., Ashraf A., Kontos D., Gavenonis S.C., Jolesz F.A., Pohl K. Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm. Proc IEEE Int Symp Biomed Imaging. 2012 May; 2012: 122-5. PMID: 28603582. PMCID: PMC5464330.
Downloaded: 270 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

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.

Additional Material
1 File (43.09kB)
Jayender-ISBI2012-fig3.jpg (43.09kB)