Share Email Print

Proceedings Paper

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Breast cancer screening with mammography and ultrasonography is expected to improve sensitivity compared with mammography alone, especially for women with dense breast. An automated breast volume scanner (ABVS) provides the operator-independent whole breast data which facilitate double reading and comparison with past exams, contralateral breast, and multimodality images. However, large volumetric data in screening practice increase radiologists’ workload. Therefore, our goal is to develop a computer-aided detection scheme of breast masses in ABVS data for assisting radiologists’ diagnosis and comparison with mammographic findings. In this study, false positive (FP) reduction scheme using deep convolutional neural network (DCNN) was investigated. For training DCNN, true positive and FP samples were obtained from the result of our initial mass detection scheme using the vector convergence filter. Regions of interest including the detected regions were extracted from the multiplanar reconstraction slices. We investigated methods to select effective FP samples for training the DCNN. Based on the free response receiver operating characteristic analysis, simple random sampling from the entire candidates was most effective in this study. Using DCNN, the number of FPs could be reduced by 60%, while retaining 90% of true masses. The result indicates the potential usefulness of DCNN for FP reduction in automated mass detection on ABVS images.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342S (3 March 2017); doi: 10.1117/12.2254581
Show Author Affiliations
Yuya Hiramatsu, Graduate School of Medicine, Gifu Univ. (Japan)
Chisako Muramatsu, Graduate School of Medicine, Gifu Univ. (Japan)
Hironobu Kobayashi, Nagoya Central Hospital (Japan)
Takeshi Hara, Graduate School of Medicine, Gifu Univ. (Japan)
Hiroshi Fujita, Graduate School of Medicine, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?