Catheter Detection and Segmentation in X-ray Images via Multi-task Learning

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Xi, Lin, Ma, Yingliang, Koland, Ethan, Howell, Sandra, Rinaldi, Aldo and Rhode, Kawal S. (2025) Catheter Detection and Segmentation in X-ray Images via Multi-task Learning.

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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.

Item Type:	Article
Uncontrolled Keywords:	cs.cv
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Visual Computing and Signal Processing Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Related URLs:	http://arxiv.org/pdf/2503.02717v1
Depositing User:	LivePure Connector
Date Deposited:	05 Mar 2025 12:30
Last Modified:	09 Mar 2025 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98682
DOI:	10.48550/arXiv.2503.02717

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