Semi-supervised image segmentation has attracted great attention recently.
The key is how to leverage unlabeled images in the training process. Most
methods maintain consistent predictions of the unlabeled images under
variations (e.g., adding noise/perturbations, or creating alternative versions)
in the image and/or model level. In most image-level variation, medical images
often have prior structure information, which has not been well explored. In
this paper, we propose novel dual structure-preserving image filterings (DSPIF)
as the image-level variations for semi-supervised medical image segmentation.
Motivated by connected filtering that simplifies image via filtering in
structure-aware tree-based image representation, we resort to the dual contrast
invariant Max-tree and Min-tree representation. Specifically, we propose a
novel connected filtering that removes topologically equivalent nodes (i.e.
connected components) having no siblings in the Max/Min-tree. This results in
two filtered images preserving topologically critical structure. Applying such
dual structure-preserving image filterings in mutual supervision is beneficial
for semi-supervised medical image segmentation. Extensive experimental results
on three benchmark datasets demonstrate that the proposed method
significantly/consistently outperforms some state-of-the-art methods. The
source codes will be publicly available.

提出了一种新颖的双结构保持图像滤波（DSPIF）作为半监督医学图像分割的图像级变化方法，通过在最大树和最小树表示中删除没有兄弟节点的拓扑等价节点，保留拓扑关键结构，在相互监督中应用这种滤波对半监督医学图像分割有益，并在三个基准数据集上的实验证明，该方法明显优于一些最先进的方法。

双结构保持图像滤波在半监督医学图像分割中的应用

Dual Structure-Preserving Image Filterings for Semi-supervised Medical  Image Segmentation

Medical image segmentation is a challenging task, made more difficult by many
datasets' limited size and annotations. Denoising diffusion probabilistic
models (DDPM) have recently shown promise in modelling the distribution of
natural images and were successfully applied to various medical imaging tasks.
This work focuses on semi-supervised image segmentation using diffusion models,
particularly addressing domain generalisation. Firstly, we demonstrate that
smaller diffusion steps generate latent representations that are more robust
for downstream tasks than larger steps. Secondly, we use this insight to
propose an improved esembling scheme that leverages information-dense small
steps and the regularising effect of larger steps to generate predictions. Our
model shows significantly better performance in domain-shifted settings while
retaining competitive performance in-domain. Overall, this work highlights the
potential of DDPMs for semi-supervised medical image segmentation and provides
insights into optimising their performance under domain shift.

本研究侧重于使用扩散模型进行半监督图像分割，特别是针对领域泛化问题，结论表明较小的扩散步骤生成的潜在表示比较大的步骤更具鲁棒性，通过利用信息密集的小步骤和较大步骤的正则化效果生成预测，该模型在领域变化设置中表现出明显较好的性能且在领域内保持有竞争力的性能，突显了 DDPM 在半监督医学图像分割中的潜力并提供了优化其在领域变化下性能的见解。

时序集成扩散模型的稳健半监督分割

Robust semi-supervised segmentation with timestep ensembling diffusion  models

We propose a novel semi-supervised image segmentation method that
simultaneously optimizes a supervised segmentation and an unsupervised
reconstruction objectives. The reconstruction objective uses an attention
mechanism that separates the reconstruction of image areas corresponding to
different classes. The proposed approach was evaluated on two applications:
brain tumor and white matter hyperintensities segmentation. Our method, trained
on unlabeled and a small number of labeled images, outperformed supervised CNNs
trained with the same number of images and CNNs pre-trained on unlabeled data.
In ablation experiments, we observed that the proposed attention mechanism
substantially improves segmentation performance. We explore two multi-task
training strategies: joint training and alternating training. Alternating
training requires fewer hyperparameters and achieves a better, more stable
performance than joint training. Finally, we analyze the features learned by
different methods and find that the attention mechanism helps to learn more
discriminative features in the deeper layers of encoders.

该研究提出了一种新颖的半监督图像分割方法，该方法同时优化了监督分割和无监督重建目标，并使用了注意机制。使用未标记的和少量标记的图像进行训练，该方法在大脑肿瘤和白质高信号分割等应用中表现优异。多次尝试后，发现交替训练比联合训练成本更低且性能更好，同时，该注意力机制有助于更好地识别深层次图像特征。