Abstract
Knee cartilage and bone segmentation is critical for physicians to
analyze and diagnose articular damage and knee osteoarthritis (OA). Deep
learning (DL) methods for medical image segmentation have largely
outperformed traditional methods, but they often need large amounts of
annotated data for model training, which is very costly and
time-consuming for medical experts, especially on 3D images. In this
paper, we report a new knee cartilage and bone segmentation framework,
KCB-Net, for 3D MR images based on sparse annotation. KCB-Net selects a
small subset of slices from 3D images for annotation, and seeks to
bridge the performance gap between sparse annotation and full
annotation. Specifically, it first identifies a subset of the most
effective and representative slices with an unsupervised scheme; it then
trains an ensemble model using the annotated slices; next, it
self-trains the model using 3D images containing pseudo-labels generated
by the ensemble method and improved by a bi-directional hierarchical
earth mover’s distance (bi-HEMD) algorithm; finally, it fine-tunes the
segmentation results using the primal-dual Internal Point Method (IPM).
Experiments on two 3D MR knee joint datasets (the Iowa dataset and
iMorphics dataset) show that our new framework outperforms
state-of-the-art methods on full annotation, and yields high quality
results even for annotation ratios as low as 5%.