  The study objective was to investigate the performance of a dedicated
convolutional neural network (CNN) optimized for wrist cartilage segmentation
from 2D MR images. CNN utilized a planar architecture and patch-based (PB)
training approach that ensured optimal performance in the presence of a limited
amount of training data. The CNN was trained and validated in twenty
multi-slice MRI datasets acquired with two different coils in eleven subjects
(healthy volunteers and patients). The validation included a comparison with
the alternative state-of-the-art CNN methods for the segmentation of joints
from MR images and the ground-truth manual segmentation. When trained on the
limited training data, the CNN outperformed significantly image-based and
patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with
manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the
representative (central coronal) slices with large amount of cartilage tissue.
Reduced performance of the network for slices with a very limited amount of
cartilage tissue suggests the need for fully 3D convolutional networks to
provide uniform performance across the joint. The study also assessed inter-
and intra-observer variability of the manual wrist cartilage segmentation
(DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based
segmentation of the wrist cartilage from MRI could facilitate research of novel
imaging markers of wrist osteoarthritis to characterize its progression and
response to therapy.
