Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning-based fully automatic segmentation of wrist cartilage in
  MR images   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.

34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph