Vascular structure segmentation plays a crucial role in medical analysis and
clinical applications. The practical adoption of fully supervised segmentation
models is impeded by the intricacy and time-consuming nature of annotating
vessels in the 3D space. This has spurred the exploration of weakly-supervised
approaches that reduce reliance on expensive segmentation annotations. Despite
this, existing weakly supervised methods employed in organ segmentation, which
encompass points, bounding boxes, or graffiti, have exhibited suboptimal
performance when handling sparse vascular structure. To alleviate this issue,
we employ maximum intensity projection (MIP) to decrease the dimensionality of
3D volume to 2D image for efficient annotation, and the 2D labels are utilized
to provide guidance and oversight for training 3D vessel segmentation model.
Initially, we generate pseudo-labels for 3D blood vessels using the annotations
of 2D projections. Subsequently, taking into account the acquisition method of
the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep
features via MIP to further improve segmentation performance. Furthermore, we
integrate confidence learning and uncertainty estimation to refine the
generated pseudo-labels, followed by fine-tuning the segmentation network. Our
method is validated on five datasets (including cerebral vessel, aorta and
coronary artery), demonstrating highly competitive performance in segmenting
vessels and the potential to significantly reduce the time and effort required
for vessel annotation. Our code is available at:
this https URL

利用最大强度投影法（MIP）将 3D 卷积降维为 2D 图像进行有效注释，然后利用 2D 标签训练三维血管分割模型并生成伪标签，进一步改进分割性能。

3D 血管分割，以最大强度投影的 2D 标注为监督

3D Vascular Segmentation Supervised by 2D Annotation of Maximum  Intensity Projection

Vessel segmentation in medical images is one of the important tasks in the
diagnosis of vascular diseases and therapy planning. Although learning-based
segmentation approaches have been extensively studied, a large amount of
ground-truth labels are required in supervised methods and confusing background
structures make neural networks hard to segment vessels in an unsupervised
manner. To address this, here we introduce a novel diffusion adversarial
representation learning (DARL) model that leverages a denoising diffusion
probabilistic model with adversarial learning, and apply it to vessel
segmentation. In particular, for self-supervised vessel segmentation, DARL
learns the background signal using a diffusion module, which lets a generation
module effectively provide vessel representations. Also, by adversarial
learning based on the proposed switchable spatially-adaptive denormalization,
our model estimates synthetic fake vessel images as well as vessel segmentation
masks, which further makes the model capture vessel-relevant semantic
information. Once the proposed model is trained, the model generates
segmentation masks in a single step and can be applied to general vascular
structure segmentation of coronary angiography and retinal images. Experimental
results on various datasets show that our method significantly outperforms
existing unsupervised and self-supervised vessel segmentation methods.

本文提出了一种使用扩散概率模型和对抗性学习的扩散对抗性表示学习（DARL）模型，用于血管分割，该模型可以在无监督和自监督的情况下学习，并且可以应用于冠状动脉和视网膜图像的血管结构分割。通过实验，我们证明该方法显著优于现有的无监督和自监督的血管分割方法。