Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field. (21st February 2023)

• Record Type: Journal Article
• Authors: Li, Yue; He, Zilong; Pan, Jiawei; Zeng, Weixiong; Liu, Jialing; Zeng, Zhaodong; Xu, Weimin; Xu, Zeyuan; Wang, Sina; Wen, Chanjuan; Zeng, Hui; Wu, Jiefang; Ma, Xiangyuan; Chen, Weiguo; Lu, Yao
• Is Part Of: Physics in medicine & biology. Volume 68:Number 4(2023)
• Publication Date: 2023-02-21
• Subjects: atypical architectural distortion -- digital breast tomosynthesis -- computer-aided detection -- adaptive receptive field; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acaba7
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective . In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT. Approach . Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases. Main results . A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement ( p < 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement. Significance . The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.