Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
<|reference_start|>Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction: Fine-grained spatio-temporal learning is crucial for freehand 3D ultrasound reconstruction. Previous works mainly resorted to the coarse-grained spatial features and the separated temporal dependency learning and struggles for fine-grained spatio-temporal learning. Mining spatio-temporal information in fine-grained scales is extremely challenging due to learning difficulties in long-range dependencies. In this context, we propose a novel method to exploit the long-range dependency management capabilities of the state space model (SSM) to address the above challenge. Our contribution is three-fold. First, we propose ReMamba, which mines multi-scale spatio-temporal information by devising a multi-directional SSM. Second, we propose an adaptive fusion strategy that introduces multiple inertial measurement units as auxiliary temporal information to enhance spatio-temporal perception. Last, we design an online alignment strategy that encodes the temporal information as pseudo labels for multi-modal alignment to further improve reconstruction performance. Extensive experimental validations on two large-scale datasets show remarkable improvement from our method over competitors.<|reference_end|>
@article{yan2024fine-grained,
    title={Fine-grained Context and Multi-modal Alignment for Freehand 3D
  Ultrasound Reconstruction},
    author={Zhongnuo Yan, Xin Yang, Mingyuan Luo, Jiongquan Chen, Rusi Chen, Lian
  Liu, Dong Ni},
    journal={arXiv preprint arXiv:2407.04242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04242},
    primaryClass={cs.CV}
}