bibtext: @InProceedings{ Wan_SIXNet_MICCAI2024,
                   author = { Wang, Xinyi and Xu, Zikang and Zhu, Heqin and Yao, Qingsong and Sun, Yiyong and Zhou, S. Kevin },
                   title = { { SIX-Net: Spatial-context Information miX-up for Electrode Landmark Detection } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Catheter ablation is a prevalent procedure for treating atrial fibrillation, primarily utilizing catheters equipped with electrodes to gather electrophysiological signals. However, the localization of catheters in fluoroscopy images presents a challenge for clinicians due to the complexity of the intervention processes. In this paper, we propose SIX-Net, a novel algorithm intending to localize landmarks of electrodes in fluoroscopy images precisely, by mixing up spatial-context information from three aspects:
  First, we propose a new network architecture specially designed for global-local spatial feature aggregation; Then, we mix up spatial correlations between segmentation and landmark detection, by sequential connections between the two tasks with the help of the Segment Anything Model; Finally, a weighted loss function is carefully designed considering the relative spatial-arrangement information among electrodes in the same image. 
  Experiment results on the test set and two clinical-challenging subsets reveal that our method outperforms several state-of-the-art landmark detection methods (~50% improvement for RF and ~25% improvement for CS).
title: SIX-Net: Spatial-context Information miX-up for Electrode Landmark Detection
authors: Wang, Xinyi Xu, Zikang Zhu, Heqin Yao, Qingsong Sun, Yiyong Zhou, S. Kevin