Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train
<|reference_start|>Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train: The complex structure of the heart leads to significant challenges in echocardiography, especially in acquisition cardiac ultrasound images. Successful echocardiography requires a thorough understanding of the structures on the two-dimensional plane and the spatial relationships between planes in three-dimensional space. In this paper, we innovatively propose a large-scale self-supervised pre-training method to acquire a cardiac structure-aware world model. The core innovation lies in constructing a self-supervised task that requires structural inference by predicting masked structures on a 2D plane and imagining another plane based on pose transformation in 3D space. To support large-scale pre-training, we collected over 1.36 million echocardiograms from ten standard views, along with their 3D spatial poses. In the downstream probe guidance task, we demonstrate that our pre-trained model consistently reduces guidance errors across the ten most common standard views on the test set with 0.29 million samples from 74 routine clinical scans, indicating that structure-aware pre-training benefits the scanning.<|reference_end|>
@article{jiang2024structure-aware,
    title={Structure-aware World Model for Probe Guidance via Large-scale
  Self-supervised Pre-train},
    author={Haojun Jiang, Meng Li, Zhenguo Sun, Ning Jia, Yu Sun, Shaqi Luo, Shiji
  Song, Gao Huang},
    journal={arXiv preprint arXiv:2406.19756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19756},
    primaryClass={cs.CV cs.AI}
}