DRL-STNet：基于解耦表示学习的跨模态医学图像分割无监督域适应
DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning
AI总结：
本文提出DRL-STNet框架，结合GAN、解耦表示学习与自训练实现跨模态医学图像分割的无监督域适应，在FLARE数据集上显著超越现有方法。
摘要：
无监督域适应（UDA）对医学图像分割至关重要，尤其是在跨模态数据场景中。UDA旨在将知识从有标签的源域迁移到无标签的目标域，从而减少对大量人工标注的依赖。本文提出了DRL-STNet，这是一个用于跨模态医学图像分割的新颖框架，它利用了生成对抗网络（GANs）、解耦表示学习（DRL）和自训练（ST）。我们的方法在GAN内利用DRL将图像从源模态转换到目标模态。然后，使用这些转换后的图像及相应的源标签对分割模型进行初始训练，并随后使用合成图像和真实图像的组合（带有伪标签和真实标签）进行迭代微调。所提出的框架在FLARE challenge数据集上的腹部器官分割中表现出卓越的性能，在Dice相似系数上超越了最先进的方法11.4%，在Normalized Surface Dice指标上超越了13.1%，分别达到了74.21%和80.69%的分数。平均运行时间为41秒，GPU内存-时间曲线下面积为11,292 MB。这些结果表明了DRL-STNet在增强跨模态医学图像分割任务方面的潜力。
英文摘要：
Unsupervised domain adaptation (UDA) is essential for medical image segmentation, especially in cross-modality data scenarios. UDA aims to transfer knowledge from a labeled source domain to an unlabeled target domain, thereby reducing the dependency on extensive manual annotations. This paper presents DRL-STNet, a novel framework for cross-modality medical image segmentation that leverages generative adversarial networks (GANs), disentangled representation learning (DRL), and self-training (ST). Our method leverages DRL within a GAN to translate images from the source to the target modality. Then, the segmentation model is initially trained with these translated images and corresponding source labels and then fine-tuned iteratively using a combination of synthetic and real images with pseudo-labels and real labels. The proposed framework exhibits superior performance in abdominal organ segmentation on the FLARE challenge dataset, surpassing state-of-the-art methods by 11.4% in the Dice similarity coefficient and by 13.1% in the Normalized Surface Dice metric, achieving scores of 74.21% and 80.69%, respectively. The average running time is 41 seconds, and the area under the GPU memory-time curve is 11,292 MB. These results indicate the potential of DRL-STNet for enhancing cross-modality medical image segmentation tasks.
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。
