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Proceedings Paper

Fully automated chest wall line segmentation in breast MRI by using context information
Author(s): Shandong Wu; Susan P. Weinstein; Emily F. Conant; A. Russell Localio; Mitchell D. Schnall; Despina Kontos
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Paper Abstract

Breast MRI has emerged as an effective modality for the clinical management of breast cancer. Evidence suggests that computer-aided applications can further improve the diagnostic accuracy of breast MRI. A critical and challenging first step for automated breast MRI analysis, is to separate the breast as an organ from the chest wall. Manual segmentation or user-assisted interactive tools are inefficient, tedious, and error-prone, which is prohibitively impractical for processing large amounts of data from clinical trials. To address this challenge, we developed a fully automated and robust computerized segmentation method that intensively utilizes context information of breast MR imaging and the breast tissue's morphological characteristics to accurately delineate the breast and chest wall boundary. A critical component is the joint application of anisotropic diffusion and bilateral image filtering to enhance the edge that corresponds to the chest wall line (CWL) and to reduce the effect of adjacent non-CWL tissues. A CWL voting algorithm is proposed based on CWL candidates yielded from multiple sequential MRI slices, in which a CWL representative is generated and used through a dynamic time warping (DTW) algorithm to filter out inferior candidates, leaving the optimal one. Our method is validated by a representative dataset of 20 3D unilateral breast MRI scans that span the full range of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) fibroglandular density categorization. A promising performance (average overlay percentage of 89.33%) is observed when the automated segmentation is compared to manually segmented ground truth obtained by an experienced breast imaging radiologist. The automated method runs time-efficiently at ~3 minutes for each breast MR image set (28 slices).

Paper Details

Date Published: 23 February 2012
PDF: 9 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831507 (23 February 2012); doi: 10.1117/12.911612
Show Author Affiliations
Shandong Wu, The Univ. of Pennsylvania (United States)
Susan P. Weinstein, The Univ. of Pennsylvania (United States)
Emily F. Conant, The Univ. of Pennsylvania (United States)
A. Russell Localio, The Univ. of Pennsylvania (United States)
Mitchell D. Schnall, The Univ. of Pennsylvania (United States)
Despina Kontos, The Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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