独立于像素级对齐数据的可靠多模态医学图像到图像转换
Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data
AI总结：
针对多模态医学图像转换中像素级对齐数据获取困难且无监督方法可靠性不足的问题，提出独立于对齐数据的MITIA模型，通过先验提取网络构建正则化项约束无监督GAN优化，在未对齐和对齐数据上均取得优于现有方法的性能。
摘要：
当前主流的多模态医学图像到图像转换方法面临一个矛盾。性能优异的有监督方法依赖像素级对齐的训练数据来约束模型优化。然而，获取像素级对齐的多模态医学图像数据集极具挑战性。无监督方法无需配对数据即可训练，但其可靠性无法得到保证。目前，尚无理想的多模态医学图像到图像转换方法能够在无需像素级对齐数据的情况下生成可靠的转换结果。本研究旨在开发一种独立于像素级对齐数据的新型医学图像到图像转换模型（MITIA），在训练数据未对齐的条件下实现可靠的多模态医学图像到图像转换。提出的MITIA模型利用由多模态医学图像配准模块和多模态未对齐误差检测模块组成的先验提取网络，最大程度地从包含未对齐误差的训练数据中提取像素级先验信息。提取的先验信息随后被用于构建正则化项，以约束无监督循环一致性GAN模型的优化，限制其解空间，从而提升生成器的性能与可靠性。我们使用包含不同未对齐误差的六个数据集和两个良好对齐的数据集训练了MITIA模型。随后，我们将该方法与其他六种最先进的图像到图像转换方法进行了比较。定量分析和定性视觉检查的结果表明，无论是在未对齐数据还是对齐数据上，MITIA均取得了优于其他竞争性最先进方法的性能。
英文摘要：
The current mainstream multi-modal medical image-to-image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel-wise aligned training data to constrain the model optimization. However, obtaining pixel-wise aligned multi-modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi-modal medical image-to-image translation method that can generate reliable translation results without the need for pixel-wise aligned data. This work aims to develop a novel medical image-to-image translation model that is independent of pixel-wise aligned data (MITIA), enabling reliable multi-modal medical image-to-image translation under the condition of misaligned training data. The proposed MITIA model utilizes a prior extraction network composed of a multi-modal medical image registration module and a multi-modal misalignment error detection module to extract pixel-level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle-consistent GAN model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well-aligned datasets. Subsequently, we compared the proposed method with six other state-of-the-art image-to-image translation methods. The results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state-of-the-art methods, both on misaligned data and aligned data.
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。
