Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attaining human-level performance with atlas location autocontext for
  anatomical landmark detection in 3D CT data   We present an efficient neural network method for locating anatomical
landmarks in 3D medical CT scans, using atlas location autocontext in order to
learn long-range spatial context. Location predictions are made by regression
to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise
application of a shallow network, thus enabling multiple volumetric heatmaps to
be predicted concurrently without prohibitive GPU memory requirements. Further,
the system allows inter-landmark spatial relationships to be exploited using a
simple overdetermined affine mapping that is robust to detection failures and
occlusion or partial views. Evaluation is performed for 22 landmarks defined on
a range of structures in head CT scans. Models are trained and validated on 201
scans. Over the final test set of 20 scans which was independently annotated by
2 human annotators, the neural network reaches an accuracy which matches the
annotator variability, with similar human and machine patterns of variability
across landmark classes.

10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-alg