A 3D Multi-Style Cross-Modality Segmentation Framework for Segmenting
  Vestibular Schwannoma and Cochlea
Yuzhou Zhuang
The crossMoDA2023 challenge aims to segment the vestibular schwannoma
(sub-divided into intra- and extra-meatal components) and cochlea regions of
unlabeled hrT2 scans by leveraging labeled ceT1 scans. In this work, we
proposed a 3D multi-style cross-modality segmentation framework for the
crossMoDA2023 challenge, including the multi-style translation and
self-training segmentation phases. Considering heterogeneous distributions and
various image sizes in multi-institutional scans, we first utilize the min-max
normalization, voxel size resampling, and center cropping to obtain fixed-size
sub-volumes from ceT1 and hrT2 scans for training. Then, we perform the
multi-style image translation phase to overcome the intensity distribution
discrepancy between unpaired multi-modal scans. Specifically, we design three
different translation networks with 2D or 2.5D inputs to generate multi-style
and realistic target-like volumes from labeled ceT1 volumes. Finally, we
perform the self-training volumetric segmentation phase in the target domain,
which employs the nnU-Net framework and iterative self-training method using
pseudo-labels for training accurate segmentation models in the unlabeled target
domain. On the crossMoDA2023 validation dataset, our method produces promising
results and achieves the mean DSC values of 72.78% and 80.64% and ASSD values
of 5.85 mm and 0.25 mm for VS tumor and cochlea regions, respectively.
Moreover, for intra- and extra-meatal regions, our method achieves the DSC
values of 59.77% and 77.14%, respectively.