Automated fractured femur segmentation using CNN

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 widely used 3D segmentation model called 3D U-Net on a dataset of 10 CT volumes. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation