  Medical image registration is an active research topic and forms a basis for
many medical image analysis tasks. Although image registration is a rather
general concept specialized methods are usually required to target a specific
registration problem. The development and implementation of such methods has
been tough so far as the gradient of the objective has to be computed. Also,
its evaluation has to be performed preferably on a GPU for larger images and
for more complex transformation models and regularization terms. This hinders
researchers from rapid prototyping and poses hurdles to reproduce research
results. There is a clear need for an environment which hides this complexity
to put the modeling and the experimental exploration of registration methods
into the foreground. With the "Autograd Image Registration Laboratory"
(AIRLab), we introduce an open laboratory for image registration tasks, where
the analytic gradients of the objective function are computed automatically and
the device where the computations are performed, on a CPU or a GPU, is
transparent. It is meant as a laboratory for researchers and developers
enabling them to rapidly try out new ideas for registering images and to
reproduce registration results which have already been published. AIRLab is
implemented in Python using PyTorch as tensor and optimization library and
SimpleITK for basic image IO. Therefore, it profits from recent advances made
by the machine learning community concerning optimization and deep neural
network models. The presented draft of this paper outlines AIRLab with first
code snippets and performance analyses. A more exhaustive introduction will
follow as a final version soon.
