Weakly-Supervised Learning via Multi-Lateral Decoder Branching for
  Guidewire Segmentation in Robot-Assisted Cardiovascular Catheterization
Olatunji Mumini Omisore Toluwanimi Akinyemi Anh Nguyen Lei Wang
Although robot-assisted cardiovascular catheterization is commonly performed
for intervention of cardiovascular diseases, more studies are needed to support
the procedure with automated tool segmentation. This can aid surgeons on tool
tracking and visualization during intervention. Learning-based segmentation has
recently offered state-of-the-art segmentation performances however, generating
ground-truth signals for fully-supervised methods is labor-intensive and time
consuming for the interventionists. In this study, a weakly-supervised learning
method with multi-lateral pseudo labeling is proposed for tool segmentation in
cardiac angiograms. The method includes a modified U-Net model with one encoder
and multiple lateral-branched decoders that produce pseudo labels as
supervision signals under different perturbation. The pseudo labels are
self-generated through a mixed loss function and shared consistency in the
decoders. We trained the model end-to-end with weakly-annotated data obtained
during robotic cardiac catheterization. Experiments with the proposed model
shows weakly annotated data has closer performance to when fully annotated data
is used. Compared to three existing weakly-supervised methods, our approach
yielded higher segmentation performance across three different cardiac
angiogram data. With ablation study, we showed consistent performance under
different parameters. Thus, we offer a less expensive method for real-time tool
segmentation and tracking during robot-assisted cardiac catheterization.