  Automatically determining the position of every slice in a CT scan is a basic
yet powerful capability allowing fast retrieval of region of interest for
visual inspection and automated analysis. Unlike conventional localization
approaches which work at the slice level, we directly localize only a fraction
of the slices and and then fit a linear model which maps slice index to its
estimated axial anatomical position based on those slices. The model is then
used to assign axial position to every slices of the scan. This approach proves
to be both computationally efficient, with a typical processing time of less
than a second per scan (regardless of its size), accurate, with a typical
median localization error of 1 cm, and robust to different noise sources,
imaging protocols, metal induced artifacts, anatomical deformations etc.
Another key element of our approach is the introduction of a mapping confidence
score. This score acts as a fail safe mechanism which allows a rejection of
unreliable localization results in rare cases of anomalous scans. Our algorithm
sets new State Of The Art results in terms of localization accuracy. It also
offers a decrease of two orders of magnitude in processing time with respect to
all published processing times. It was designed to be invariant to various scan
resolutions, scan protocols, patient orientations, strong artifacts and various
deformations and abnormalities. Additionally, our algorithm is the first one to
the best of our knowledge which supports the entire body from head to feet and
is not confined to specific anatomical region. This algorithm was tested on
thousands of scans and proves to be very reliable and useful as a preprocessing
stage for many applications.
