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. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410

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Abstract

Purpose: 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. Methods: 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. Results: The proposed method has been validated on both public and private datasets for single-task catheter segmentation and multi-task catheter segmentation and detection. The performance of our method is also compared with existing state-of-the-art methods, demonstrating significant improvements, with a mean J of 64.37/63.97 and with average precision over all IoU thresholds of 84.15/83.13, respectively, for detection and segmentation multi-task on the validation and test sets of the catheter detection and segmentation dataset. Conclusions: Our approach achieves a good balance between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.

Item Type:	Article
Additional Information:	Acknowledgements: This work was supported by EPSRC UK (EP/X023826/1).
Uncontrolled Keywords:	catheter detection,deep learning,multi-task learning,object segmentation,x-ray fluoroscopy,surgery,biomedical engineering,radiology nuclear medicine and imaging,computer vision and pattern recognition,computer science applications,health informatics,computer graphics and computer-aided design ,/dk/atira/pure/subjectarea/asjc/2700/2746
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://www.scopus.com/inward/record.url?... http://arxiv.org/pdf/2503.02717v1
Depositing User:	LivePure Connector
Date Deposited:	05 Mar 2025 12:30
Last Modified:	23 Jul 2025 10:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98682
DOI:	10.48550/arXiv.2503.02717

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