StyleSeg V2：通过无优化的配准误差感知实现鲁棒的脑组织单次分割
StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
AI总结：
针对脑组织单次分割中配准误差影响分割模型的问题，提出StyleSeg V2，通过镜像输入的对称不一致性无成本感知配准误差，优化分割与风格变换保真度，在三个数据集上性能显著优于现有方法。
摘要：
脑组织单次分割需要迭代训练配准-分割（reg-seg）双模型，其中配准模型旨在通过扭曲一张经过精细标注的图谱（atlas），为分割模型提供未标注图像的伪掩码。然而，不完善的配准模型会导致图像与掩码不对齐，进而对分割模型产生负面影响。近期的StyleSeg通过用图谱的扭曲副本替代未标注图像来绕过这一瓶颈，但需要通过风格变换引入多样化的图像模式。在此，我们提出StyleSeg V2，它继承自StyleSeg，但具备感知配准误差的能力。其核心动机是：良好的配准对镜像图像会呈现镜像式的表现。因此，几乎无需额外成本，StyleSeg V2只需对输入进行镜像处理（即对脑部进行对称翻转），就能让配准模型自身“指出”对齐错误的区域，而配准误差就是原始输入与镜像输入的输出之间的对称不一致性。相应地，StyleSeg V2不仅让分割模型能够利用未标注图像中对齐正确的区域，还通过根据配准误差对局部变换强度进行加权，提升了风格变换后扭曲图谱图像的保真度。在三个公开数据集上的实验结果表明，我们提出的StyleSeg V2以显著优势优于其他现有最优方法，且相比StyleSeg，其平均Dice系数提升了至少2.4%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
