Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Mass Segmentation in Automated 3D Breast Ultrasound using Deep Learning

Authors

Hamed Fayyaz 1
Mohsen Soryani 2
Ehsan Koozegar 3
Tao Tan 4

1 M.Sc. Student, Artificial Intelligence Department, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

2 Associate Professor, Artificial Intelligence Department, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

3 Ph.D. Student, Artificial Intelligence Department, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

4 Postdoctoral Researcher, Department of Radiology and Nuclear Medicine, Radboud University, Nijmegen, Netherlands

https://doi.org/10.22041/ijbme.2018.90857.1384
Abstract
Automated 3D breast ultrasound (ABUS) is a novel system for breast screening‎. ‎It has been proposed as a supplementary modality to mammography for detection and diagnosis of breast cancers‎. ‎Although ABUS has better performance for dense breasts‎, ‎reading ABUS images is time-consuming and exhausting‎. ‎A computer-aided detection (CAD) system can be helpful for interpretation of ABUS images‎. ‎Mass Segmentation in CADe and CADx systems play the leading role because it affects the performance of succeeding stages‎. ‎Besides‎, ‎it is a very challenging task because of the vast variety in size‎, ‎shape, and texture of masses‎. ‎Moreover, imbalanced datasets make segmentation harder‎. ‎A novel mass segmentation approach based on deep learning is introduced in this paper‎. ‎The deep network that is used in this study for image segmentation is inspired by U-net which has been used broadly for dense segmentation in recent years‎‎. ‎Performance was determined using a dataset of 50 masses including 38 malignant and 12 benign masses‎.

Keywords

Breast Cancer‎
‎Automated Diagnosis‎
‎Tumor‎
‎3D ABUS‎
Mass Segmentation
‎Deep Learning

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 12, Issue 2
Summer 2018
Pages 137-146

  • Receive Date 28 July 2018
  • Revise Date 26 September 2018
  • Accept Date 27 September 2018

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