Catheter Detection and Segmentation in X-ray Images via Multi-task Learning
Journal:
arXiv
Published Date:
Mar 4, 2025
Abstract
Automated detection and segmentation of surgical devices, such as catheters
or wires, in X-ray fluoroscopic images have the potential to enhance image
guidance in minimally invasive heart surgeries. In this paper, we present a
convolutional neural network model that integrates a resnet architecture with
multiple prediction heads to achieve real-time, accurate localization of
electrodes on catheters and catheter segmentation in an end-to-end deep
learning framework. We also propose a multi-task learning strategy in which our
model is trained to perform both accurate electrode detection and catheter
segmentation simultaneously. A key challenge with this approach is achieving
optimal performance for both tasks. To address this, we introduce a novel
multi-level dynamic resource prioritization method. This method dynamically
adjusts sample and task weights during training to effectively prioritize more
challenging tasks, where task difficulty is inversely proportional to
performance and evolves throughout the training process. Experiments on both
public and private datasets have demonstrated that the accuracy of our method
surpasses the existing state-of-the-art methods in both single segmentation
task and in the detection and segmentation multi-task. Our approach achieves a
good trade-off between accuracy and efficiency, making it well-suited for
real-time surgical guidance applications.
