3D Vascular Segmentation Supervised by 2D Annotation of Maximum
  Intensity Projection
Zhanqiang Guo Zimeng Tan Jianjiang Feng Jie Zhou
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:
https://github.com/gzq17/Weakly-Supervised-by-MIP.