Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively. The code is available at https://github.com/LOUEY233/Toward-Mutual-Information}{https://github.com/LOUEY233/Toward-Mutual-Information
翻译:未受监督的域适应办法最近成功地在各种医疗图像分割任务中取得了成功。所报告的工作往往通过调整域-异性特征和尽量减少特定领域的差异来应对域转移问题。当特定领域之间和不同领域之间的差异不大时,这一战略效果良好。然而,这些不同成像模式的一般化能力仍是一个重大挑战。本文介绍了UDA-VAE++++,这是一个不受监督的心脏分割区域适应框架,其内含紧凑损失功能的缩缩放性。为了估计这一新较低的约束性,我们开发了一个与全球估计器、一个局部估测器(SMIE)的新型结构相互信息估计(SMIE)块,以及一个先前的信息匹配估计器,以尽量扩大重建和分割任务之间的相互信息。具体地说,我们设计了一个新的顺序再校准计划,使信息从低分辨率潜伏空间流向高分辨率潜在空间并进行差异校正。关于心脏分割数据集基准的全面实验表明,我们的模型比以往的状态-艺术定性和定量校准性高度/MOY23/MUAYSUDR/MUDRIFF3/MUI/MIFF3/MUY3/LSUDRIS/MIS3/L/ODRIFF/MIS3/L/ODRIFF/ODRIS3/L/ODR/OBR/MIS3/LVR/MIS3/L/OD/GIS3/L/ADRRIS3/L/ADRIFF/MA/MA/ODRIS/OFIFIFIFIFF/GRIS。