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Proceedings Paper

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN
Author(s): Xiangrong Zhou; Takuya Kano; Hiromi Koyasu; Shuo Li; Xinxin Zhou; Takeshi Hara; Masayuki Matsuo; Hiroshi Fujita
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Paper Abstract

This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast <25%, 25%–50%, 50%–75%, and >75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342Q (3 March 2017); doi: 10.1117/12.2254320
Show Author Affiliations
Xiangrong Zhou, Graduate School of Medicine, Gifu Univ. (Japan)
Takuya Kano, Graduate School of Medicine, Gifu Univ. (Japan)
Hiromi Koyasu, Graduate School of Medicine, Gifu Univ. (Japan)
Shuo Li, Schulich School of Medicine and Dentistry, Univ. of Western Ontario London (Canada)
Xinxin Zhou, Schulich School of Medicine and Dentistry, Univ. of Western Ontario London (Canada)
Takeshi Hara, Graduate School of Medicine, Gifu Univ. (Japan)
Masayuki Matsuo, Nagoya Bunri 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; Nicholas A. Petrick, Editor(s)

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