Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks

Purpose: Early detection of breast cancer has a significant effect on reducing its mortality rate. For this purpose, automated three-dimensional breast ultrasound (3-D ABUS) has been recently used alongside mammography. The 3-D volume produced in this imaging system includes many slices. The radiologist must review all the slices to find the mass, a time-consuming task with a high probability of mistakes. Therefore, many computer-aided detection (CADe) systems have been developed to assist radiologists in this task. In this paper, we propose a novel CADe system for mass detection in 3-D ABUS images. Methods: The proposed system includes two cascaded convolutional neural networks. The goal of the first network is to achieve the highest possible sensitivity, and the second network's goal is to reduce false positives while maintaining high sensitivity. In both networks, an improved version of 3-D U-Net architecture is utilized in which two types of modified Inception modules are used in the encoder section. In the second network, new attention units are also added to the skip connections that receive the results of the first network as saliency maps. Results: The system was evaluated on a dataset containing 60 3-D ABUS volumes from 43 patients and 55 masses. A sensitivity of 91.48% and a mean false positive of 8.85 per patient were achieved. Conclusions: The suggested mass detection system is fully automatic without any user interaction. The results indicate that the sensitivity and the mean FP per patient of the CADe system outperform competing techniques.