  The analysis of single CT slices extracted at the third lumbar vertebra (L3)
has garnered significant clinical interest in the past few years, in particular
in regards to quantifying sarcopenia (muscle loss). In this paper, we propose
an efficient method to automatically detect the L3 slice in 3D CT images. Our
method works with images with a variety of fields of view, occlusions, and
slice thicknesses. 3D CT images are first converted into 2D via Maximal
Intensity Projection (MIP), reducing the dimensionality of the problem. The MIP
images are then used as input to a 2D fully-convolutional network to predict
the L3 slice locations in the form of 2D confidence maps. In addition we
propose a variant architecture with less parameters allowing 1D confidence map
prediction and slightly faster prediction time without loss of accuracy.
Quantitative evaluation of our method on a dataset of 1006 3D CT images yields
a median error of 1mm, similar to the inter-rater median error of 1mm obtained
from two annotators, demonstrating the effectiveness of our method in
efficiently and accurately detecting the L3 slice.
