Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jul 13, 2024

Abstract

PURPOSE: Early detection of breast cancer has a significant effect on reducing its mortality rate. For this purpose, automated three-dimensional breast ultrasound (3-D ABUS) has been recently used alongside mammography. The 3-D volume produced in this imaging system includes many slices. The radiologist must review all the slices to find the mass, a time-consuming task with a high probability of mistakes. Therefore, many computer-aided detection (CADe) systems have been developed to assist radiologists in this task. In this paper, we propose a novel CADe system for mass detection in 3-D ABUS images.

Authors

  • Sepideh Barekatrezaei
    School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. Electronic address: s_barekat@comp.iust.ac.ir.
  • Ehsan Kozegar
    Department of Computer Engineering and Engineering Sciences, Faculty of Technology and Engineering, University of Guilan, Rudsar-Vajargah, Guilan, Iran. Electronic address: kozegar@guilan.ac.ir.
  • Masoumeh Salamati
    Department of Reproductive Imaging, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran. Electronic address: dr.m.salamati@gmail.com.
  • Mohsen Soryani
    School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. Electronic address: soryani@iust.ac.ir.

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