  The lack of a comprehensive high-resolution atlas of the cerebellum has
hampered studies of cerebellar involvement in normal brain function and
disease. A good representation of the tightly foliated aspect of the cerebellar
cortex is difficult to achieve because of the highly convoluted surface and the
time it would take for manual delineation. The quality of manual segmentation
is influenced by human expert judgment, and automatic labelling is constrained
by the limited robustness of existing segmentation algorithms. The
20umisotropic BigBrain dataset provides an unprecedented high resolution
framework for semantic segmentation compared to the 1000um(1mm) resolution
afforded by magnetic resonance imaging. To dispense with the manual annotation
requirement, we propose to train a model to adaptively transfer the annotation
from the cerebellum on the Allen Brain Human Brain Atlas to the BigBrain in an
unsupervised manner, taking into account the different staining and spacing
between sections. The distinct visual discrepancy between the Allen Brain and
BigBrain prevents existing approaches to provide meaningful segmentation masks,
and artifacts caused by sectioning and histological slice preparation in the
BigBrain data pose an extra challenge. To address these problems, we propose a
two-stage framework where we first transfer the Allen Brain cerebellum to a
space sharing visual similarity with the BigBrain. We then introduce a
self-training strategy with a confidence map to guide the model learning from
the noisy pseudo labels iteratively. Qualitative results validate the
effectiveness of our approach, and quantitative experiments reveal that our
method can achieve over 2.6% loss reduction compared with other approaches.
