Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation

Dong Wei; Susan Weinstein; Meng-Kang Hsieh; Lauren Pantalone; Mitchell Schnall; Despina Kontos

doi:10.1117/12.2248626

24 February 2017 Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, Mitchell Schnall, Despina Kontos

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 1013314 (2017) https://doi.org/10.1117/12.2248626
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, Mitchell Schnall, and Despina Kontos "Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013314 (24 February 2017); https://doi.org/10.1117/12.2248626

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