  Image registration plays an important role in comparing images. It is
particularly important in analyzing medical images like CT, MRI, PET, etc. to
quantify different biological samples, to monitor disease progression and to
fuse different modalities to support better diagnosis. The recent emergence of
tissue clearing protocols enable us to take images at cellular level
resolution. Image registration tools developed for other modalities are
currently unable to manage images of such extreme high resolution. The recent
popularity of deep learning based methods in the computer vision community
justifies a rigorous investigation of deep-learning based methods on tissue
cleared images along with their traditional counterparts. In this paper, we
investigate and compare the performance of a deep learning based registration
method with traditional optimization based methods on samples from
tissue-clearing methods. From the comparative results it is found that a
deep-learning based method outperforms all traditional registration tools in
terms of registration time and has achieved promising registration accuracy.
