Spatiotemporal breast mass detection network (md-net) in 4D DCE-MRI images

Abstract

Automatic mass detection in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) helps to reduce the workload of radiologists and improves diagnostic accuracy. However, most of the existing methods rely on hand-crafted features followed by rule-based or shallow machine learning based detection methods. Due to the limited expressive power of hand-crafted features, the diagnostic performances of existing methods are usually unsatisfactory. In this work, we aim to leverage recent deep learning techniques for breast lesion detection and propose the Spatiotemporal Breast Mass Detection Networks (MD-Nets) to detect the masses in the 4D DCE-MRI images automatically. Simulating the clinical diagnosis process, we initially generate image-based candidates from all individual images and then construct a spatiotemporal 4D data to classify mass by using the convolutional long short-term memory network (ConvLSTM) to incorporate kinetic and spatial characteristics. Moreover, we collect a DCE-MRI dataset containing 21,294 annotated images from 172 studies. In experiments, we achieve an AUC of 0.9163 with a sensitivity of 0.8655 and a specificity of 0.8452, which verifies the effectiveness of our method.