Automated and real-time segmentation of suspicious breast masses using convolutional neural network

Viksit Kumar, Jeremy M. Webb, Adriana Gregory, Max Denis, Duane D. Meixner, Mahdi Bayat, Dana H. Whaley, Mostafa Fatemi, Azra Alizad

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7 Scopus citations

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.

Original languageEnglish (US)
Article numbere0195816
JournalPLoS One
Volume13
Issue number5
DOIs
StatePublished - May 1 2018

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ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Kumar, V., Webb, J. M., Gregory, A., Denis, M., Meixner, D. D., Bayat, M., Whaley, D. H., Fatemi, M., & Alizad, A. (2018). Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS One, 13(5), [e0195816]. https://doi.org/10.1371/journal.pone.0195816