# Catheter detection and segmentation in X-ray images via multi-task learning

## TL;DR

This paper introduces a deep learning model that detects and segments catheters in X-ray images, improving accuracy and efficiency for real-time surgical guidance.

## Contribution

A novel multi-level dynamic resource prioritization method for multi-task learning in catheter detection and segmentation.

## Key findings

- The proposed method achieves a mean J of 64.37/63.97 for detection and segmentation multi-task.
- Average precision over all IoU thresholds reaches 84.15/83.13 on validation and test sets.
- The model balances accuracy and efficiency, suitable for real-time surgical applications.

## 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.

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 \documentclass[12pt]{minimal}
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\begin{document}$$\mathcal {J}$$\end{document}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.

Our approach achieves a good balance between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.