Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study.

Abstract

The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images of complex intertrochanteric fractures for orthopedic surgeons. This study was based on 160 hip CT scans (43,510 images) of complex fractures of three types based on the Evans-Jensen classification (40 cases of type 3 (IIA) fractures, 80 cases of type 4 (IIB)fractures, and 40 cases of type 5 (III)fractures) retrospectively. The images were randomly split into two groups to construct a training set of 120 CT scans (32,045 images) and a testing set of 40 CT scans (11,465 images). A deep learning model was built into a cascaded architecture composed by a convolutional neural network (CNN) for location of the fracture ROI and another CNN for recognition and segmentation of individual fragments within the ROI. The accuracy of object detection and dice coefficient of segmentation of individual fragments were used to evaluate model performance. The model yielded an average accuracy of 89.4% for individual fragment recognition and an average dice coefficient of 90.5% for segmentation in CT images. The results demonstrated the feasibility of recognition and segmentation of individual fragments in complex intertrochanteric fractures with a deep learning approach. Altogether, these promising results suggest the potential of our model to be applied to many clinical scenarios.