Deep Dual-view Network with Smooth Loss for Spinal Metastases Classification

Spinal metastases have a high incidence among cancer patients and may later develop to metastatic spinal cord compression (MSCC). Early detection of spinal metastases is critical for optimal treatment. The diagnosis is usually facilitated with computed tomography (CT) scans, which requires considerable efforts from well-trained radiologist. In this paper, we explore automatic spinal metastases classification based on CT images. Considering the unique characteristics of spinal CT images, a novel Deep Dual-view Network is proposed which contains two branches: a X-Y Conv Branch to extract the features for each individual cross-sectional image slice, and a Z Conv Branch to capture z-direction features from neighboring images. The features from the two branches are then fused to generate the final prediction, which imitates the doctor’s way of combining cross sections with sagittal or coronal sections. Considering a tumor usually presents in multiple consecutive image slices, a smooth loss is introduced to maintain the label consistency of adjacent images. To validate the proposed approach, we collect a dataset of 316 patients with spinal metastases. Experimental results on this data set have demonstrated the effectiveness and the robustness of the proposed Deep Dual-view network.