DUGi

Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@2072@@303090

Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact (p<0.05p<0.05) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of 0.69±0.110.69±0.11 with a mean False Positive ratio 0.35±0.140.35±0.14 has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is r2=0.960r2=0.960 (p=0.05p=0.05). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient r2=0.967r2=0.967 (p<0.05p<0.05) for prior and r2=0.956r2=0.956 (p<0.05p<0.05) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is partially supported by the SMARTER project funded by the Ministry of Economy and Competitiveness of Spain, under project reference DPI2015-68442-R. O.D. is funded by the SCARtool project (H2020-MSCA-IF-2014, reference 657875), a research funded by the European Union within the Marie Sklodowska-Curie Innovative Training Networks. R.A. is funded by the support of the Secretariat of Universities and Research, Ministry of Economy and Knowledge, Government of Catalonia Ref. ECO/1794/2015 FIDGR-2016

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