Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Online Quality Control of Synthetic CTs   Accurate MR-to-CT synthesis is a requirement for MR-only workflows in
radiotherapy (RT) treatment planning. In recent years, deep learning-based
approaches have shown impressive results in this field. However, to prevent
downstream errors in RT treatment planning, it is important that deep learning
models are only applied to data for which they are trained and that generated
synthetic CT (sCT) images do not contain severe errors. For this, a mechanism
for online quality control should be in place. In this work, we use an ensemble
of sCT generators and assess their disagreement as a measure of uncertainty of
the results. We show that this uncertainty measure can be used for two kinds of
online quality control. First, to detect input images that are outside the
expected distribution of MR images. Second, to identify sCT images that were
generated from suitable MR images but potentially contain errors. Such
automatic online quality control for sCT generation is likely to become an
integral part of MR-only RT workflows.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex