Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913436388032512 |
|---|---|
| author | Jiang, Haojun Li, Meng Sun, Zhenguo Jia, Ning Sun, Yu Luo, Shaqi Song, Shiji Huang, Gao |
| author_facet | Jiang, Haojun Li, Meng Sun, Zhenguo Jia, Ning Sun, Yu Luo, Shaqi Song, Shiji Huang, Gao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19756 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train Jiang, Haojun Li, Meng Sun, Zhenguo Jia, Ning Sun, Yu Luo, Shaqi Song, Shiji Huang, Gao Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2406.19756 |