Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis

Author:

Li Yue,He Zilong,Lu Yao,Ma Xiangyuan,Guo Yanhui,Xie Zheng,Qin Genggeng,Xu Weimin,Xu Zeyuan,Chen Weiguo,Chen Haibin

Abstract

Abstract Computer aided detection (CADe) for breast lesions can provide an important reference for radiologists in breast cancer screening. Architectural distortion (AD) is a type of breast lesion that is difficult to detect. A majority of CADe methods focus on detecting the radial pattern, which is a main characteristic of typical ADs. However, a few atypical ADs do not exhibit such a pattern. To improve the performance of CADe for typical and atypical ADs, we propose a deep-learning-based model that used mammary gland distribution as prior information to detect ADs in digital breast tomosynthesis (DBT). First, information about gland distribution, including the Gabor magnitude, the Gabor orientation field, and a convergence map, were produced using a bank of Gabor filters and convergence measures. Then, this prior information and an original slice were input into a Faster R-CNN detection network to obtain the 2-D candidates for each slice. Finally, a 3-D aggregation scheme was employed to fuse these 2-D candidates as 3-D candidates for each DBT volume. Retrospectively, 64 typical AD volumes, 74 atypical AD volumes, and 127 normal volumes were collected. Six-fold cross-validation and mean true positive fraction (MTPF) were used to evaluate the model. Compared to an existing convergence-based model, our proposed model achieved an MTPF of 0.53 ± 0.04, 0.61 ± 0.05, and 0.45 ± 0.04 for all DBT volumes, typical + normal volumes, and atypical + normal volumes, respectively. These results were significantly better than those of 0.36 ± 0.03, 0.46 ± 0.04, and 0.28 ± 0.04 for a convergence-based model (p ≪ 0.01). These results indicate that employing the prior information of gland distribution and a deep learning method can improve the performance of CADe for AD.

Funder

Guangdong Province Key Laboratory of Computational Science

Fundamental Research Funds for the Central Universities

Science and Technology Planning Project of Guangdong Province

Guangzhou Science and Technology Creative Project

Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education

Clinical Research Program of Nanfang Hospital, Southern Medical University

Natural Science Foundation of Guangdong Province, China

National Key R&D Program of China

National Natural Science Foundation of China

Science and Technology Innovative Project of Guangdong Province

Construction Project of Shanghai Key Laboratory of Molecular Imaging

Publisher

IOP Publishing

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference28 articles.

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