13 March 2017 A comparison of automated versus manual segmentation of breast UST transmission images to measure breast volume and sound speed
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Abstract
Ultrasound tomography (UST) is an emerging breast imaging modality that can be used to quantitatively measure breast density. However, the sound speed images that are used in this analysis must first be segmented in order to accurately parse any quantitative information. Previously, this segmentation has been done manually, but this is time consuming, especially when dealing with a large number of images that must be masked. An automated masking algorithm has been developed that applies thresholding and morphological operators to UST attenuation images to automatically create masks that separate the breast tissue from the water bath. An initial set of images was tested using this algorithm to fine tune settings and very good agreement was achieved. However, when the optimized settings were applied to a larger dataset of 286 images, the robustness of the algorithm was tested. The manual masks measured a larger volume (921 cm3) than the automated masks (713 cm3), but fortunately, the difference in mean sound speed was much smaller (1449 m/s versus 1448 m/s). A majority of the automated masks (72.7%) had a measured Dice similarity coefficient (DSC) of greater than 0.8 which indicates that there was good to great overlap in the volumes of tissue created by the automated method. This algorithm shows promise to be used as a tool to quickly and effectively measure breast density.
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Mark Sak, Neb Duric, Peter Littrup, Katelyn Westerberg, "A comparison of automated versus manual segmentation of breast UST transmission images to measure breast volume and sound speed", Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 101391H (13 March 2017); doi: 10.1117/12.2254482; https://doi.org/10.1117/12.2254482
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