Appearance constrained semi-automatic segmentation from DCE-MRI is reproducible and feasible for breast cancer radiomics: A feasibility study Journal Article

   

Authors:	Veeraraghavan, H.; Dashevsky, B. Z.; Onishi, N.; Sadinski, M.; Morris, E.; Deasy, J. O.; Sutton, E. J.
Article Title:	Appearance constrained semi-automatic segmentation from DCE-MRI is reproducible and feasible for breast cancer radiomics: A feasibility study
Abstract:	We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM's segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2- vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83). © 2018 The Author(s).
Journal Title:	Scientific Reports
Volume:	8
ISSN:	2045-2322
Publisher:	Nature Publishing Group
Date Published:	2018-03-19
Start Page:	4838
Language:	English
DOI:	10.1038/s41598-018-22980-9
PROVIDER:	scopus
PMCID:	PMC5859113
PUBMED:	29556054
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044297485&doi=10.1038%2fs41598-018-22980-9&partnerID=40&md5=fbfabbadce22921e0a08171b4b5b624a
Notes:	Article -- Export Date: 1 May 2018 -- Source: Scopus

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