  Automatic body part recognition for CT slices can benefit various medical
image applications. Recent deep learning methods demonstrate promising
performance, with the requirement of large amounts of labeled images for
training. The intrinsic structural or superior-inferior slice ordering
information in CT volumes is not fully exploited. In this paper, we propose a
convolutional neural network (CNN) based Unsupervised Body part Regression
(UBR) algorithm to address this problem. A novel unsupervised learning method
and two inter-sample CNN loss functions are presented. Distinct from previous
work, UBR builds a coordinate system for the human body and outputs a
continuous score for each axial slice, representing the normalized position of
the body part in the slice. The training process of UBR resembles a
self-organization process: slice scores are learned from inter-slice
relationships. The training samples are unlabeled CT volumes that are abundant,
thus no extra annotation effort is needed. UBR is simple, fast, and accurate.
Quantitative and qualitative experiments validate its effectiveness. In
addition, we show two applications of UBR in network initialization and anomaly
detection.
