One-shot segmentation of brain tissues is typically a dual-model iterative
learning: a registration model (reg-model) warps a carefully-labeled atlas onto
unlabeled images to initialize their pseudo masks for training a segmentation
model (seg-model); the seg-model revises the pseudo masks to enhance the
reg-model for a better warping in the next iteration. However, there is a key
weakness in such dual-model iteration that the spatial misalignment inevitably
caused by the reg-model could misguide the seg-model, which makes it converge
on an inferior segmentation performance eventually. In this paper, we propose a
novel image-aligned style transformation to reinforce the dual-model iterative
learning for robust one-shot segmentation of brain tissues. Specifically, we
first utilize the reg-model to warp the atlas onto an unlabeled image, and then
employ the Fourier-based amplitude exchange with perturbation to transplant the
style of the unlabeled image into the aligned atlas. This allows the subsequent
seg-model to learn on the aligned and style-transferred copies of the atlas
instead of unlabeled images, which naturally guarantees the correct spatial
correspondence of an image-mask training pair, without sacrificing the
diversity of intensity patterns carried by the unlabeled images. Furthermore,
we introduce a feature-aware content consistency in addition to the image-level
similarity to constrain the reg-model for a promising initialization, which
avoids the collapse of image-aligned style transformation in the first
iteration. Experimental results on two public datasets demonstrate 1) a
competitive segmentation performance of our method compared to the
fully-supervised method, and 2) a superior performance over other
state-of-the-art with an increase of average Dice by up to 4.67%. The source
code is available at: this https URL

通过图像对齐的样式转换，将双模型迭代学习强化以实现强大的大脑组织一次分割，并保证空间对应关系的正确性