  For diagnosis of shoulder illness, it is essential to look at the morphology
deviation of scapula and humerus from the medical images that are acquired from
Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is
time-consuming and costly because the reduction of the physical distance
between image slices causes prolonged scanning time. Moreover, due to the lack
of training images, images from various sources must be utilized, which creates
the issue of high variance across the dataset. Also, there are human errors
among the images due to the fact that it is hard to take the spatial
relationship into consideration when labeling the 3D image in low resolution.
In order to combat all obstacles stated above, we develop a fully automated
algorithm for segmenting the humerus and scapula bone from coarsely scanned and
low-resolution MR images and a recursive learning framework that iterative
utilize the generated labels for reducing the errors among segmentations and
increase our dataset set for training the next round network. In this study, 50
MR images are collected from several institutions and divided into five
mutually exclusive sets for carrying five-fold cross-validation. Contours that
are generated by the proposed method demonstrated a high level of accuracy when
compared with ground truth and the traditional method. The proposed neural
network and the recursive learning scheme improve the overall quality of the
segmentation on humerus and scapula on the low-resolution dataset and reduced
incorrect segmentation in the ground truth, which could have a positive impact
on finding the cause of shoulder pain and patient's early relief.
