@article{5965f08da4c847c18ff2c609bc7c2e62,
title = "Automated and real-time segmentation of suspicious breast masses using convolutional neural network",
abstract = "In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13–55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.",
author = "Viksit Kumar and Webb, {Jeremy M.} and Adriana Gregory and Max Denis and Meixner, {Duane D.} and Mahdi Bayat and Whaley, {Dana H.} and Mostafa Fatemi and Azra Alizad",
note = "Funding Information: Research reported in this publication was supported by grants R01CA148994, R01CA148994-S1, R01CA168575, R01CA195527, and R01CA174723 from the National Cancer Institute. The authors are grateful to Cynthia Andrist, our clinical coordinator, Kathryn Schinke-Bierly and Jennifer Poston for administrative support. Research reported in this publication was supported by National Institute of health grants R01CA148994, R01CA148994-S1, R01CA168575, R01CA195527, and R01CA174723 from the National Cancer Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We gratefully acknowledge the support of Amazon web services for the donation of credits used for this research. Publisher Copyright: {\textcopyright} 2018 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
year = "2018",
month = may,
doi = "10.1371/journal.pone.0195816",
language = "English (US)",
volume = "13",
journal = "PloS one",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "5",
}