Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization

Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking endovascular tools during procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is laborintensive, time consuming, and costly. In this study, we developed a weakly-supervised learning method that is based on multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations to augment the available partial annotation for model training. A mixed loss function with shared consistency was adapted for this purpose. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the weakly-supervised model could perform closer to fully-supervised models. Furthermore, the proposed multi-lateral approach outperforms three well known weakly-supervised learning methods, offering the highest segmentation performance across the three angiogram datasets. Numerous ablation studies confirmed the model's consistent performance under different settings. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean segmentation time of 35.26±11.29 ms per frame. This study provides a fast, stable, and less expensive method for segmentation and visualization of endovascular tools in robot-assisted cardiac catheterization.