Deep learning on mammary glands distribution for architectural distortion detection in digital breast tomosynthesis.

Deep learning on mammary glands distribution for architectural distortion detection in digital breast tomosynthesis. Phys Med Biol. 2020 Jun 02;: Authors: Li Y, He Z, Lu Y, Ma X, Guo Y, Xie Z, Xu Z, Chen W, Chen H Abstract Computer aided detection (CADe) for breast lesion 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 proposed a deep-learning-based model that used the mammary glands distribution as prior information to detect ADs in digital breast tomosynthesis (DBT). First, information about gland distributions, including Gabor magnitude, Gabor orientation field, and convergence map, were produced using a bank of Gabor filters and convergence measures. Then, this prior information and original slice were input into a Faster-RCNN 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...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research