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Automatic Segmentation of Invasive Breast Carcinomas from Dynamic Contrast-Enhanced MRi using Time Series Analysis

Institution:
1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department Breast Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
John Wiley & Sons, Inc.
Publication Date:
Aug-2014
Journal:
J Magn Reson Imaging
Volume Number:
40
Issue Number:
2
Pages:
467-75
Citation:
J Magn Reson Imaging. 2014 Aug;40(2):467-75.
PubMed ID:
24115175
PMCID:
PMC3962815
Keywords:
DCE-MRI, Breast Carcinomas, linear dynamic system, Segmentation, time-series analysis
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 RR019703/RR/NCRR NIH HHS/United States
P01 CA067165/CA/NCI NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Jayender J., Chikarmane S., Jolesz F.A., Gombos E. Automatic Segmentation of Invasive Breast Carcinomas from Dynamic Contrast-Enhanced MRi using Time Series Analysis. J Magn Reson Imaging. 2014 Aug;40(2):467-75. PMID: 24115175. PMCID: PMC3962815.
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PURPOSE: To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. MATERIALS AND METHODS: Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). RESULTS: The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95. CONCLUSION: The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI.

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