  Total hip arthroplasty (THA) is a widely used surgical procedure in
orthopedics. For THA, it is of clinical significance to analyze the bone
structure from the CT images, especially to observe the structure of the
acetabulum and femoral head, before the surgical procedure. For such bone
structure analyses, deep learning technologies are promising but require
high-quality labeled data for the learning, while the data labeling is costly.
We address this issue and propose an efficient data annotation pipeline for
producing a deep learning-oriented dataset. Our pipeline consists of
non-learning-based bone extraction (BE) and acetabulum and femoral head
segmentation (AFS) and active-learning-based annotation refinement (AAR). For
BE we use the classic graph-cut algorithm. For AFS we propose an improved
algorithm, including femoral head boundary localization using first-order and
second-order gradient regularization, line-based non-maximum suppression, and
anatomy prior-based femoral head extraction. For AAR, we refine the
algorithm-produced pseudo labels with the help of trained deep models: we
measure the uncertainty based on the disagreement between the original pseudo
labels and the deep model predictions, and then find out the samples with the
largest uncertainty to ask for manual labeling. Using the proposed pipeline, we
construct a large-scale bone structure analyses dataset from more than 300
clinical and diverse CT scans. We perform careful manual labeling for the test
set of our data. We then benchmark multiple state-of-the art deep
learning-based methods of medical image segmentation using the training and
test sets of our data. The extensive experimental results validate the efficacy
of the proposed data annotation pipeline. The dataset, related codes and models
will be publicly available at https://github.com/hitachinsk/THA.
