  One-shot segmentation of brain tissue requires training
registration-segmentation (reg-seg) dual-model iteratively, where reg-model
aims to provide pseudo masks of unlabeled images for seg-model by warping a
carefully-labeled atlas. However, the imperfect reg-model induces image-mask
misalignment, poisoning the seg-model subsequently. Recent StyleSeg bypasses
this bottleneck by replacing the unlabeled images with their warped copies of
atlas, but needs to borrow the diverse image patterns via style transformation.
Here, we present StyleSeg V2, inherited from StyleSeg but granted the ability
of perceiving the registration errors. The motivation is that good registration
behaves in a mirrored fashion for mirrored images. Therefore, almost at no
cost, StyleSeg V2 can have reg-model itself "speak out" incorrectly-aligned
regions by simply mirroring (symmetrically flipping the brain) its input, and
the registration errors are symmetric inconsistencies between the outputs of
original and mirrored inputs. Consequently, StyleSeg V2 allows the seg-model to
make use of correctly-aligned regions of unlabeled images and also enhances the
fidelity of style-transformed warped atlas image by weighting the local
transformation strength according to registration errors. The experimental
results on three public datasets demonstrate that our proposed StyleSeg V2
outperforms other state-of-the-arts by considerable margins, and exceeds
StyleSeg by increasing the average Dice by at least 2.4%.
