KEYWORDS: Image segmentation, Breast, Digital breast tomosynthesis, Computer aided diagnosis and therapy, Mammography, Digital mammography, Tomography, Neural networks
Semantic segmentation of breast images is typically performed as a preprocessing step for breast cancer detection by Computer Aided Diagnosis (CAD) systems. While most literature on region segmentation is based on conventional techniques like line estimation, thresholding and atlas-based approaches, such methods may have problems with generalisation. This paper investigates a robust multi-vendor breast region segmentation system for full field digital mammograms (FFDM) and digital breast tomography (DBT) using a U-Net neural network. Additionally, the effect of adding attention gates to the U-Net architecture was analysed. The proposed networks were trained and tested in a cross-validation setting on in-house FFDM/DBT data and the public INbreast datasets, comprising over 10,000 FFDM and 3,500 DBT images from five different vendors. Dice scores were obtained in the range 0.978 - 0.985, with slightly higher scores for the architecture that includes attention gates.
KEYWORDS: Mammography, Breast, Image processing, Magnetic resonance imaging, Breast cancer, Convolutional neural networks, Digital imaging, Convolution, Classification systems
Breast density is an important factor in breast cancer screening. Methods exist to measure the volume of dense breast tissue from 2D mammograms. However, these methods can only be applied to raw mammograms. Breast density classification methods that have been developed for processed mammograms are commonly based on radiologist Breast Imaging and Reporting Data System (BI-RADS) annotations. Unfortunately, such labels are subjective and may introduce personal bias and inter-reader discrepancy. In order to avoid such limitations, this paper presents a method for estimation of percent dense tissue volume (PDV) from processed full field digital mammograms (FFDM) using a deep learning approach. A convolutional neural network (CNN) was implemented to carry out a regression task of estimating PDV using density measurement on raw FFDM as a ground truth. The dataset used for training, validation, and testing (Set A) includes over 2000 clinical cases from 3 different vendors. Our results show a high correlation of the predicted PDV to raw measurements, with a Spearman’s correlation coefficient of r=0.925. The CNN was also tested on an independent set of 97 clinical cases (Set B) for which PDV measurements from FFDM and MRI were available. CNN predictions on Set B showed a high correlation with both raw FFDM and MRI data (r=0.897 and r=0.903, respectively). Set B had radiologist annotated BI-RADS labels, which agreed with the estimated values to a high degree, showing the ability of our CNN to make a distinction between different BI-RADS categories comparable to methods applied to raw mammograms.
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