The domain adaptation approach has gained significant acceptance in
transferring styles across various vendors and centers, along with filling the
gaps in modalities. However, multi-center application faces the challenge of
the difficulty of domain adaptation due to their intra-domain differences. We
focus on introducing a fine-grained unsupervised framework for domain
adaptation to facilitate cross-modality segmentation of vestibular schwannoma
(VS) and cochlea. We propose to use a vector to control the generator to
synthesize a fake image with given features. And then, we can apply various
augmentations to the dataset by searching the feature dictionary. The diversity
augmentation can increase the performance and robustness of the segmentation
model. On the CrossMoDA validation phase Leaderboard, our method received a
mean Dice score of 0.765 and 0.836 on VS and cochlea, respectively.

引入细粒度无监督框架以实现跨模态的前庭神经硬膜瘤和耳蜗分割，通过使用向量控制生成器并应用多样增强技术来提高分割模型的性能和鲁棒性。在 CrossMoDA 验证阶段排行榜上，我们的方法分别在前庭神经硬膜瘤和耳蜗上得到了 0.765 和 0.836 的平均 Dice 得分。

基于细粒度无监督跨模态域自适应的前庭神经瘤分割

Fine-Grained Unsupervised Cross-Modality Domain Adaptation for  Vestibular Schwannoma Segmentation

Domain shift and label scarcity heavily limit deep learning applications to
various medical image analysis tasks. Unsupervised domain adaptation (UDA)
techniques have recently achieved promising cross-modality medical image
segmentation by transferring knowledge from a label-rich source domain to an
unlabeled target domain. However, it is also difficult to collect annotations
from the source domain in many clinical applications, rendering most prior
works suboptimal with the label-scarce source domain, particularly for few-shot
scenarios, where only a few source labels are accessible. To achieve efficient
few-shot cross-modality segmentation, we propose a novel
transformation-consistent meta-hallucination framework, meta-hallucinator, with
the goal of learning to diversify data distributions and generate useful
examples for enhancing cross-modality performance. In our framework,
hallucination and segmentation models are jointly trained with the
gradient-based meta-learning strategy to synthesize examples that lead to good
segmentation performance on the target domain. To further facilitate data
hallucination and cross-domain knowledge transfer, we develop a self-ensembling
model with a hallucination-consistent property. Our meta-hallucinator can
seamlessly collaborate with the meta-segmenter for learning to hallucinate with
mutual benefits from a combined view of meta-learning and self-ensembling
learning. Extensive studies on MM-WHS 2017 dataset for cross-modality cardiac
segmentation demonstrate that our method performs favorably against various
approaches by a lot in the few-shot UDA scenario.

本文提出了一种基于元学习和数据幻觉的元幻觉框架，旨在解决医学图像分析中标签稀缺和领域转移问题，尤其是在少样本场景下，通过数据幻觉和交叉域知识传递来增强性能。对跨模态心脏分割的广泛实验表明，该方法在少样本无监督领域自适应情况下比其他方法表现更出色。

元幻影生成器：迈向小样本跨模态心脏图像分割

Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image  Segmentation

For medical image segmentation, imagine if a model was only trained using MR
images in source domain, how about its performance to directly segment CT
images in target domain? This setting, namely generalizable cross-modality
segmentation, owning its clinical potential, is much more challenging than
other related settings, e.g., domain adaptation. To achieve this goal, we in
this paper propose a novel dual-normalization model by leveraging the augmented
source-similar and source-dissimilar images during our generalizable
segmentation. To be specific, given a single source domain, aiming to simulate
the possible appearance change in unseen target domains, we first utilize a
nonlinear transformation to augment source-similar and source-dissimilar
images. Then, to sufficiently exploit these two types of augmentations, our
proposed dual-normalization based model employs a shared backbone yet
independent batch normalization layer for separate normalization. Afterward, we
put forward a style-based selection scheme to automatically choose the
appropriate path in the test stage. Extensive experiments on three publicly
available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal
Multi-Organ datasets, have demonstrated that our method outperforms other
state-of-the-art domain generalization methods. Code is available at
this https URL

本文提出了一种基于双标准化模型的，借助源域中类似和不类似的图像数据在目标域中进行通用分割的方法，旨在解决跨模态分割中不同领域之间的特征差异问题，并在 BraTS、Cross-Modality Cardiac 和 Abdominal Multi-Organ 等公共领域数据集上进行实验和测试，表明此方法优于其他最先进的领域泛化方法。