Fracture fixation surgeries require a careful and well thought out surgical plan, mainly due to the wide range of possibilities in the fracture types and available choices in fixation constructs. There is considerable interest in virtual 3D planning tools ranging from 3D visualization, interactive fracture reduction and bio-mechanical analysis of fracture fixation construct stability to arrive at optimal plan. One of the key steps prior to reconstructing 3D fractures is accurate fracture segmentation which can be tedious and time consuming even with semi-automated tools. In this paper, we report preliminary results from our attempt to fully automate the segmentation of fractured bone using deep learning. We performed experiments using the widely used 3D segmentation model called 3D U-Net on a dataset of 14 CT volumes. The dataset is randomly divided into train, validation and test splits comprising 7, 3 and 4 volumes respectively. Even with a small training set of femur fractures, we were able to achieve a mean dice score of 0.861 with a mean sensitivity of 0.899. The model was able to capture the challenging fracture regions and could cleanly separate the femur head and socket. Apart from this, we also studied the impact of different loss functions on the network’s performance. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation. Further improvement is expected with a larger collection of such fractured samples.