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Proceedings Paper

Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation
Author(s): Dong Wei; Susan Weinstein; Meng-Kang Hsieh; Lauren Pantalone; Mitchell Schnall; Despina Kontos
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Paper Abstract

Whole breast segmentation is the first step in quantitative analysis of breast MR images. This task is challenging due mainly to the chest-wall line’s (CWL) spatially varying appearance and nearby distracting structures, both being complex. In this paper, we propose an automatic three-dimensional (3-D) segmentation method of whole breast in sagittal MR images. This method distinguishes itself from others in two main aspects. First, it reformulates the challenging problem of CWL localization into an equivalence that searches for an optimal smooth depth field and so fully utilizes the 3-D continuity of the CWLs. Second, it employs a localized self- adapting algorithm to adjust to the CWL’s spatial variation. Experimental results on real patient data with expert-outlined ground truth show that the proposed method can segment breasts accurately and reliably, and that its segmentation is superior to that of previously established methods.

Paper Details

Date Published: 24 February 2017
PDF: 6 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013314 (24 February 2017); doi: 10.1117/12.2248626
Show Author Affiliations
Dong Wei, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Susan Weinstein, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Lauren Pantalone, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Mitchell Schnall, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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