Architectural distortion detection in digital breast tomosynthesis with adaptive receptive field and adaptive convolution kernel shape

Abstract

Architectural distortion (AD) is one of the breast abnormal signs in medical imaging and it is hard to be detected in clinic because of its subtle appearance and similar intensity with surrounding tissues. We previously developed a deep-learning-based model for AD detection in digital breast tomosynthesis (DBT). However, for atypical ADs, the model’s detection performance was not good enough because atypical ADs do not have a radial pattern, which is the main characteristic of AD. Considering that radiologists always take surrounding tissues’ information as reference to locate atypical ADs, an ideal model should not only adapt to the different shape of atypical ADs, but also have a large receptive field. In this study, deformable convolution kernel was employed to establish a novel deep-learning-based AD detection model. A dataset of 265 DBT volumes including 64 typical ADs, 74 atypical ADs and 127 normal volumes were collected for model evaluation. Mean true positive fraction (MTPF) was used as figure-of-merit. The results of six-fold cross-validation showed that after involving deformable convolution, the MTPF improved from 0.53±0.04 to 0.56±0.04 (p=0.028) and the number of false positives (FPs) at 80% sensitivity reduced from 1.95 to 1.09. Especially for atypical AD, the MTPF improved from 0.45±0.05 to 0.51±0.04 (p=0.01) and the number of FPs at 80% sensitivity reduced from 4.79 to 1.51. These results showed that this model has potential to assist radiologists locate more suspicious ADs and improve their diagnosis efficiency.