  Automatic vertebrae identification and localization from arbitrary CT images
is challenging. Vertebrae usually share similar morphological appearance.
Because of pathology and the arbitrary field-of-view of CT scans, one can
hardly rely on the existence of some anchor vertebrae or parametric methods to
model the appearance and shape. To solve the problem, we argue that one should
make use of the short-range contextual information, such as the presence of
some nearby organs (if any), to roughly estimate the target vertebrae; due to
the unique anatomic structure of the spine column, vertebrae have fixed
sequential order which provides the important long-range contextual information
to further calibrate the results.
  We propose a robust and efficient vertebrae identification and localization
system that can inherently learn to incorporate both the short-range and
long-range contextual information in a supervised manner. To this end, we
develop a multi-task 3D fully convolutional neural network (3D FCN) to
effectively extract the short-range contextual information around the target
vertebrae. For the long-range contextual information, we propose a multi-task
bidirectional recurrent neural network (Bi-RNN) to encode the spatial and
contextual information among the vertebrae of the visible spine column. We
demonstrate the effectiveness of the proposed approach on a challenging dataset
and the experimental results show that our approach outperforms the
state-of-the-art methods by a significant margin.
