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

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

doi:10.1371/journal.pone.0195816

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.

Highlights

Breast cancer is the most common cancer among American women after skin cancer, and is a leading cause of death with an estimate of 40,450 cases in 2016 [1, 2]
More than half of the cases of breast cancer occur in the developing world, with a mortality rate inversely related to the country’s wealth [2]
Various imaging modalities are used for screening breast tissue with the goal of early detection

Summary

Introduction

Breast cancer is the most common cancer among American women after skin cancer, and is a leading cause of death with an estimate of 40,450 cases in 2016 [1, 2]. More than half of the cases of breast cancer occur in the developing world, with a mortality rate inversely related to the country’s wealth [2]. Various imaging modalities are used for screening breast tissue with the goal of early detection (e.g., mammography, US, and magnetic resonance imaging). Annual breast cancer mammography screening is recommended by the American Cancer Society for women between the ages of 45 and 54, with biennial screening after the age of 54 [3]. Younger, premenopausal women have denser breasts compared to older, postmenopausal women, which makes interpretation

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