Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Automatic Spleen Length Measurement in Sickle Cell
  Disease Patients   Sickle Cell Disease (SCD) is one of the most common genetic diseases in the
world. Splenomegaly (abnormal enlargement of the spleen) is frequent among
children with SCD. If left untreated, splenomegaly can be life-threatening. The
current workflow to measure spleen size includes palpation, possibly followed
by manual length measurement in 2D ultrasound imaging. However, this manual
measurement is dependent on operator expertise and is subject to intra- and
inter-observer variability. We investigate the use of deep learning to perform
automatic estimation of spleen length from ultrasound images. We investigate
two types of approach, one segmentation-based and one based on direct length
estimation, and compare the results against measurements made by human experts.
Our best model (segmentation-based) achieved a percentage length error of
7.42%, which is approaching the level of inter-observer variability
(5.47%-6.34%). To the best of our knowledge, this is the first attempt to
measure spleen size in a fully automated way from ultrasound images.

12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex