用细粒度风格迁移生成多样T2脑部影像，提升分割模型泛化能力。
IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation
- 通过对比学习分离解剖与风格特征，实现3D图像风格自动建模。
- 在跨模态MRI适配中生成更多样化的合成图像，提升下游分割精度。
- 适合医学图像领域需处理设备差异的场景，尤其关注精准分割任务。
从T2 MRI中分割前庭神经鞘瘤和耳蜗具有重要临床价值，但标注成本高。域适应（DA）常用于连接有标签的增强T1与无标签的T2数据集。现有方法聚焦跨域对齐，却忽视目标域内部的变异性——同一域内图像因扫描仪、场强及采集协议不同而差异显著。忽略此变异会导致合成图像同质化，降低下游分割模型的泛化能力。为此，我们提出IntraStyler，一种3D无配对图像翻译方法，可自动发现无需预定义子域的细粒度域内风格，并基于每张图像的风格参考生成多样化目标域图像。我们设计了一个3D风格编码器，采用新型对比学习目标提取仅与风格相关的嵌入。IntraStyler基于跨模态医学图像域适应挑战赛（CrossMoDA）第一名方案进一步改进，生成更丰富的合成数据，实现更可靠的下游分割。代码已公开于https://github.com/MedICL-VU/IntraStyler。
原文摘要 · Abstract (English)
Segmentation of vestibular schwannoma and cochlea from T2 MRI is clinically important yet annotation-intensive. Domain adaptation (DA) has been widely adopted to bridge the gap between labeled contrast-enhanced T1 and unlabeled T2 datasets. While existing methods focus on cross-domain alignment, intra-domain variability within the target domain remains largely overlooked. Images from the same domain may vary substantially due to different scanners, field strengths, and acquisition protocols. Ignoring this variability produces homogeneous synthetic images that limit the generalizability of downstream segmentation models. To address this, we propose IntraStyler, a 3D unpaired image translation method that automatically discovers fine-grained intra-domain styles without any predefined sub-domains, and synthesizes diverse target domain images using per-image style references. To this end, we design a 3D style encoder trained with a novel contrastive learning objective to extract style-only embeddings disentangled from anatomy. IntraStyler is built upon the 1st place CrossMoDA challenge solution and further advances it, generating more diverse synthetic data and achieving more reliable downstream segmentation. Code is available at https://github.com/MedICL-VU/IntraStyler.
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