Authors
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
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
Main Subjects
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