通过配准引导的一致性与解耦学习提升医学图像合成性能
Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning
AI总结：
针对医学图像合成中训练不对齐噪声的问题，提出配准引导一致性结合解耦学习的方法，通过形变场约束、风格解剖解耦及一致性损失提升合成效果，经两类数据集验证性能更优。
摘要：
医学图像合成因训练过程中的不对齐噪声仍颇具挑战性。现有方法已尝试通过引入配准引导模块来应对这一挑战，但这类方法往往忽略了合成模块与配准模块的任务特定约束，可能导致训练期间无论配准模块如何作用，合成模块仍会生成与目标图像空间不对齐的图像。为此，本文提出配准引导的一致性机制，并结合解耦学习用于医学图像合成。所提出的配准引导一致性架构通过在合成前后施加相同的形变场，同时借助对齐损失强制输出一致性，来强化合成与配准模块的任务特异性。此外，合成模块被设计为具备解耦不同模态间解剖结构与特定风格的能力，本文还引入解剖一致性损失，进一步促使合成模块在潜空间中保留几何完整性。在内部腹部CECT-CT数据集与公开盆腔MR-CT数据集上开展的实验，均验证了所提方法的优越性。
英文摘要：
Medical image synthesis remains challenging due to misalignment noise during training. Existing methods have attempted to address this challenge by incorporating a registration-guided module. However, these methods tend to overlook the task-specific constraints on the synthetic and registration modules, which may cause the synthetic module to still generate spatially aligned images with misaligned target images during training, regardless of the registration module's function. Therefore, this paper proposes registration-guided consistency and incorporates disentanglement learning for medical image synthesis. The proposed registration-guided consistency architecture fosters task-specificity within the synthetic and registration modules by applying identical deformation fields before and after synthesis, while enforcing output consistency through an alignment loss. Moreover, the synthetic module is designed to possess the capability of disentangling anatomical structures and specific styles across various modalities. An anatomy consistency loss is introduced to further compel the synthetic module to preserve geometrical integrity within latent spaces. Experiments conducted on both an in-house abdominal CECT-CT dataset and a publicly available pelvic MR-CT dataset have demonstrated the superiority of the proposed method.
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。
