Open AccessAutomated and real-time segmentation of suspicious breast masses using convolutional neural network
Author(s) -
Viksit Kumar,
Jeremy Webb,
Adriana Gregory,
Max Denis,
Duane D. Meixner,
Mahdi Bayat,
Dana H. Whaley,
Mostafa Fatemi,
Azra Alizad
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0195816
Subject(s) - sørensen–dice coefficient , segmentation , convolutional neural network , breast ultrasound , artificial intelligence , computer science , biopsy , medicine , pattern recognition (psychology) , ultrasound , radiology , image segmentation , breast cancer , mammography , cancer
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.