Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration.

Robert B GruppMathias UnberathCong GaoRachel A HegemanRyan J MurphyClayton P AlexanderYoshito OtakeBenjamin A McArthurMehran ArmandRussell H Taylor

Published in: International journal of computer assisted radiology and surgery (2020)

We have created the first accurately annotated, non-synthetic, dataset of hip fluoroscopy. By using these annotations as training data for neural networks, state-of-the-art performance in fluoroscopic segmentation and landmark localization was achieved. Integrating these annotations allows for a robust, fully automatic, and efficient intraoperative registration during fluoroscopic navigation of the hip.

Keyphrases

neural network
deep learning
total hip arthroplasty
machine learning
convolutional neural network
rna seq
virtual reality