Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train

• URL: http://arxiv.org/abs/2406.19756v2
• Date: Fri, 19 Jul 2024 07:15:07 GMT
• Title: Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train
• Authors: Haojun Jiang, Meng Li, Zhenguo Sun, Ning Jia, Yu Sun, Shaqi Luo, Shiji Song, Gao Huang,
• Abstract summary: Successful echocardiography requires a thorough understanding of the structures on the two-dimensional plane and the spatial relationships between planes in three-dimensional space. We propose a large-scale self-supervised pre-training method to acquire a cardiac structure-aware world model.
• Score: 66.35766658717205
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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