arXiv 2503.009452025-03-02eess.IVcs.AIcs.CV

跨模态医学图像合成用于改进肝脏分割

Cross Modality Medical Image Synthesis for Improving Liver Segmentation

COMSATS University Islamabad（COMSATS大学伊斯兰堡校区）；University of Stirling（斯特灵大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）

Muhammad Rafiq, Hazrat Ali, Ghulam Mujtaba, Zubair Shah, Shoaib Azmat

备注：: Submitted to Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

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AI总结：

针对医学图像标注数据集稀缺问题，提出基于形变不变网络EssNet的两阶段跨模态图像合成方法，通过CT生成合成MRI并结合真实数据训练U-Net，使肝脏分割的IoU提升1.17%。

摘要：

基于深度学习的医学图像计算机辅助诊断（CAD）需要大型数据集。然而，缺乏大型公开可用的标注数据集限制了基于深度学习的CAD系统的发展。生成对抗网络（GAN），特别是CycleGAN，可以在没有配对训练数据的情况下生成新的跨域图像。然而，大多数基于CycleGAN的合成方法缺乏克服输入数据和生成数据之间对齐和不对称性的潜力。我们提出了一种两阶段技术，使用腹部CT的跨模态转换来合成腹部MRI。我们展示了合成数据可以帮助提高肝脏分割网络的性能。我们使用一种受CycleGAN启发的形变不变网络EssNet，通过对未配对CT图像进行跨模态图像变换，增加了腹部MRI图像的数量。随后，我们将合成MRI图像与原始MRI图像相结合，并利用它们来提高U-Net在肝脏分割任务上的准确性。我们首先在真实MRI图像上训练U-Net，然后在真实和合成MRI图像上训练。因此，通过比较这两种场景，我们实现了U-Net性能的提升。总而言之，在交并比（IoU）上取得的提升为1.17%。结果表明，该方法具有解决医学成像中数据稀缺挑战的潜力。

英文摘要：

Deep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labeled datasets limits the development of deep learning-based CAD systems. Generative Adversarial Networks (GANs), in particular, CycleGAN, can be used to generate new cross-domain images without paired training data. However, most CycleGAN-based synthesis methods lack the potential to overcome alignment and asymmetry between the input and generated data. We propose a two-stage technique for the synthesis of abdominal MRI using cross-modality translation of abdominal CT. We show that the synthetic data can help improve the performance of the liver segmentation network. We increase the number of abdominal MRI images through cross-modality image transformation of unpaired CT images using a CycleGAN inspired deformation invariant network called EssNet. Subsequently, we combine the synthetic MRI images with the original MRI images and use them to improve the accuracy of the U-Net on a liver segmentation task. We train the U-Net on real MRI images and then on real and synthetic MRI images. Consequently, by comparing both scenarios, we achieve an improvement in the performance of U-Net. In summary, the improvement achieved in the Intersection over Union (IoU) is 1.17%. The results show potential to address the data scarcity challenge in medical imaging.

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