Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis

Abstract

Architectural distortion (AD) is one of the important breast abnormal signs in digital breast tomosynthesis (DBT). It is hard to be detected due to its subtle appearance and similar intensity with surrounding tissue. To assist radiologists to detect ADs, a single-view based computer-aided detection model in DBT was developed by us previously. In this study, considering the fact that radiologists always use information from craniocaudal (CC) and mediolateral oblique (MLO) views of DBT simultaneously for better diagnosis of each breast in clinic, we further develop a multi-view based AD detection model in DBT that combines the information from the two views. In this model, AD candidates in each view are detected by our previous AD detection model. Anatomical position priors of AD candidates in the two views are considered through establishing a 3D anatomical coordinate system. A multi-view based classifier is trained to fuse information from the two views and distinguish the true AD candidates. A dataset of 196 CC-MLO DBT pairs were collected with IRB approval, 101 of them contained ADs and the remaining were negative pairs. Ten-fold cross-validation showed that after involving our proposed multi-view method, the sensitivities of AD detection at 1, 2, 3 and 4 false positive predictions per DBT pairs improved from 0.66, 0.73, 0.77 and 0.79 to 0.69, 0.77, 0.78, and 0.83, respectively. The results showed that the multi-view based model achieved better detection performance than single-view based model. This model has potential to assist radiologists in detection of ADs in DBT.