  With the advances of deep learning, many medical image segmentation studies
achieve human-level performance when in fully supervised condition. However, it
is extremely expensive to acquire annotation on every data in medical fields,
especially on magnetic resonance images (MRI) that comprise many different
contrasts. Unsupervised methods can alleviate this problem; however, the
performance drop is inevitable compared to fully supervised methods. In this
work, we propose a self-training based unsupervised-learning framework that
performs automatic segmentation of Vestibular Schwannoma (VS) and cochlea on
high-resolution T2 scans. Our method consists of 4 main stages: 1)
VS-preserving contrast conversion from contrast-enhanced T1 scan to
high-resolution T2 scan, 2) training segmentation on generated T2 scans with
annotations on T1 scans, and 3) Inferring pseudo-labels on non-annotated real
T2 scans, and 4) boosting the generalizability of VS and cochlea segmentation
by training with combined data (i.e., real T2 scans with pseudo-labels and
generated T2 scans with true annotations). Our method showed mean Dice score
and Average Symmetric Surface Distance (ASSD) of 0.8570 (0.0705) and 0.4970
(0.3391) for VS, 0.8446 (0.0211) and 0.1513 (0.0314) for Cochlea on
CrossMoDA2021 challenge validation phase leaderboard, outperforming most other
approaches.
