Mr. Zheng Xie Profile

Zheng Xie

at Sun Yat-Sen Univ

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Proceedings Article | 16 March 2020 Presentation + Paper

Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis

Yue Li, Zheng Xie, Zilong He, Xiangyuan Ma, Yanhui Guo, Weiguo Chen, Yao Lu

Proceedings Volume 11314, 1131417 (2020) https://doi.org/10.1117/12.2549143

KEYWORDS: Digital breast tomosynthesis, Architectural distortion, Breast cancer

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Architectural distortion (AD) is one of the most important potentially ominous signs of breast cancer. As a 3D imaging, digital breast tomosynthesis (DBT) is an accurate tool to detect AD. We developed a deep learning approach for AD detection guided by mammary gland spatial pattern (MGSP) in DBT. The approach consists of two stages: 2D detection and 3D aggregation. In 2D detection, prior MGSP information is obtained first. It includes 1) magnitude image and orientation field map produced from Gabor filters and 2) mammary gland convergence map. Second, Faster-RCNN detection network is employed. Region proposal network extracts features and determines locations of AD candidates and the soft classifier is used for reducing false positives. In 3D aggregation, a region fusion strategy is designed to fuse 2D candidates into 3D candidates. For evaluation, 265 DBT volumes (138 with ADs and 127 without any lesion) were collected from 68 patients. Free response receiver operating characteristic curve was obtained and the mean true positive fraction (MTPF) was used as the figure-of-merit of model performance. Compared with a baseline model based on convergence measure, the six-fold cross validation results showed that our proposed approach achieved MTPF of 0.50 ± 0.04, while the baseline achieved 0.37 ± 0.03. The improvement of our approach was statistically significant (p≪0.001).

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