Hematoma Segmentation Using Dilated Convolutional Neural Network

Traumatic brain injury (TBI) is a global health challenge. Accurate and fast automatic detection of hematoma in the brain is essential for TBI diagnosis and treatment. In this study, we developed a fully automated system to detect and segment hematoma regions in head Computed Tomography (CT) images of patients with acute TBI. We adapted the structure of a fully convolutional network by introducing dilated convolution and removing down-sampling and up-sampling layers. Skip layers are also used to combine low-level features and high-level features. By integrating the information from different scales without losing spatial resolution, the network can perform more accurate segmentation. Our final hematoma segmentations achieved the Dice, sensitivity, and specificity of 0.62, 0.81, and 0.96, respectively, which outperformed the results from previous methods.