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Proceedings Paper

Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification
Author(s): Tao Tan; Jan van Zelst; Wei Zhang; Ritse M. Mann; Bram Platel; Nico Karssemeijer
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Paper Abstract

Computer-aided detection (CAD) systems are expected to improve effectiveness and efficiency of radiologists in reading automated 3D breast ultrasound (ABUS) images. One challenging task on developing CAD is to reduce a large number of false positives. A large amount of false positives originate from acoustic shadowing caused by ribs. Therefore determining the location of the chestwall in ABUS is necessary in CAD systems to remove these false positives. Additionally it can be used as an anatomical landmark for inter- and intra-modal image registration. In this work, we extended our previous developed chestwall segmentation method that fits a cylinder to automated detected rib-surface points and we fit the cylinder model by minimizing a cost function which adopted a term of region cost computed from a thoracic volume classifier to improve segmentation accuracy. We examined the performance on a dataset of 52 images where our previous developed method fails. Using region-based cost, the average mean distance of the annotated points to the segmented chest wall decreased from 7.57±2.76 mm to 6.22±2.86 mm.art.

Paper Details

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351Y (20 March 2014); doi: 10.1117/12.2043552
Show Author Affiliations
Tao Tan, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Jan van Zelst, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Wei Zhang, QView Medical, Inc. (United States)
Ritse M. Mann, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Bram Platel, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Nico Karssemeijer, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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