Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification

Tao Tan; Jan van Zelst; Wei Zhang; Ritse M. Mann; Bram Platel; Nico Karssemeijer

doi:10.1117/12.2043552

20 March 2014 Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification

Tao Tan, Jan van Zelst, Wei Zhang, Ritse M. Mann, Bram Platel, Nico Karssemeijer

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90351Y (2014) https://doi.org/10.1117/12.2043552
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Tao Tan, Jan van Zelst, Wei Zhang, Ritse M. Mann, Bram Platel, and Nico Karssemeijer "Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351Y (20 March 2014); https://doi.org/10.1117/12.2043552

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KEYWORDS

Image segmentation

Breast

Ultrasonography

3D image processing

Chest

Computer aided diagnosis and therapy

3D modeling

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