3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection
<|reference_start|>3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection: 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.<|reference_end|>
@article{guo20243d,
    title={3D Vascular Segmentation Supervised by 2D Annotation of Maximum
  Intensity Projection},
    author={Zhanqiang Guo and Zimeng Tan and Jianjiang Feng and Jie Zhou},
    journal={arXiv preprint arXiv:2402.12128},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12128},
    primaryClass={cs.CV}
}