Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:520-524. doi: 10.1109/EMBC48229.2022.9871048.
Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for cross-modality medical images. In this work, we present a new unsupervised domain adaptation approach called Multi-Stage GAN (MSGAN) to tackle the problem of domain shift for CT and MRI segmentation tasks. We adopt the multi-stage strategy in parallel to avoid information loss and transfer rough styles on low-resolution feature maps to the detailed textures on high-resolution feature maps. In detail, the style layers map the learnt style codes from the Gaussian noise to the input features in order to synthesize images with different styles. We validate the proposed method for cross-modality medical image segmentation tasks on two public datasets, and the results demonstrate the effectiveness of our method. Clinical relevance- This technique paves the way to translate cross-modality images (MRI and CT) and it can also mitigate the performance degradation when applying deep neural networks in a cross-domain scenario.
领域自适应已成为一个重要的研究课题，因为源域训练的神经网络由于领域转移，通常在目标域中的性能较差，特别是对于跨模态医学图像。在这项工作中，我们提出了一种新的无监督领域自适应方法，称为多阶段 GAN（MSGAN），以解决 CT 和 MRI 分割任务中的领域转移问题。我们采用多阶段策略并行处理，以避免信息丢失，并将低分辨率特征图上的粗略样式转移到高分辨率特征图上的详细纹理。具体来说，样式层将从高斯噪声中学到的样式代码映射到输入特征，以合成具有不同样式的图像。我们在两个公共数据集上验证了该方法在跨模态医学图像分割任务中的有效性，结果表明了该方法的有效性。临床相关性- 这项技术为跨模态图像（MRI 和 CT）的转换铺平了道路，并且在跨域场景中应用深度神经网络时，它还可以减轻性能下降。
