bibtext: @InProceedings{ Yan_Finegrained_MICCAI2024,
                   author = { Yan, Zhongnuo and Yang, Xin and Luo, Mingyuan and Chen, Jiongquan and Chen, Rusi and Liu, Lian and Ni, Dong },
                   title = { { Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
authors: Yan, Zhongnuo Yang, Xin Luo, Mingyuan Chen, Jiongquan Chen, Rusi Liu, Lian Ni, Dong