  The crossMoDA challenge aims to automatically segment the vestibular
schwannoma (VS) tumor and cochlea regions of unlabeled high-resolution T2 scans
by leveraging labeled contrast-enhanced T1 scans. The 2022 edition extends the
segmentation task by including multi-institutional scans. In this work, we
proposed an unpaired cross-modality segmentation framework using data
augmentation and hybrid convolutional networks. Considering heterogeneous
distributions and various image sizes for multi-institutional scans, we apply
the min-max normalization for scaling the intensities of all scans between -1
and 1, and use the voxel size resampling and center cropping to obtain
fixed-size sub-volumes for training. We adopt two data augmentation methods for
effectively learning the semantic information and generating realistic target
domain scans: generative and online data augmentation. For generative data
augmentation, we use CUT and CycleGAN to generate two groups of realistic T2
volumes with different details and appearances for supervised segmentation
training. For online data augmentation, we design a random tumor signal
reducing method for simulating the heterogeneity of VS tumor signals.
Furthermore, we utilize an advanced hybrid convolutional network with
multi-dimensional convolutions to adaptively learn sparse inter-slice
information and dense intra-slice information for accurate volumetric
segmentation of VS tumor and cochlea regions in anisotropic scans. On the
crossMoDA2022 validation dataset, our method produces promising results and
achieves the mean DSC values of 72.47% and 76.48% and ASSD values of 3.42 mm
and 0.53 mm for VS tumor and cochlea regions, respectively.
