Multi-Task Learning for Real-Time Catheter Detection and Segmentation in X-ray Images

Overview

This study presents a novel deep learning framework that simultaneously detects and segments catheters in X-ray images using a multi-task learning approach with dynamic resource prioritization. The proposed model achieves superior accuracy and real-time performance compared to state-of-the-art methods on both public and private datasets.

Background

Minimally invasive heart surgeries rely heavily on X-ray fluoroscopy to guide catheters and pacing leads, which are highly visible in X-ray images. However, anatomical structures like heart chambers and blood vessels are not clearly visible, necessitating the overlay of 3D models from CT or MR scans for enhanced guidance. Accurate catheter detection and segmentation are critical for motion compensation, registration of 3D models, and potential robotic autonomy in procedures. Traditional methods based on shape models and vessel enhancement filters face challenges due to image artifacts and wire-like structures, motivating the development of deep learning-based multi-task models.

Data Highlights

The model uses an encoder-decoder architecture with a ResNet backbone producing 512-dimensional embedded features. An attention module enhances these features before decoding to a stride 4 resolution. The output includes center heatmaps for detection and segmentation masks, enabling simultaneous localization and delineation of catheters. The multi-level dynamic resource prioritization strategy allocates learning resources to difficult samples and tasks, improving generalization and performance beyond existing methods.

Key Findings

• A novel convolutional neural network architecture enables simultaneous catheter detection via center heatmaps and segmentation in X-ray images.
• The multi-level dynamic resource prioritization training strategy effectively focuses learning on difficult samples and tasks at both sample and task levels.
• The encoder-decoder design with attention modules preserves low-level details and refines high-level global context for improved feature representation.
• The model achieves superior accuracy and real-time inference speed compared to state-of-the-art two-stage and one-stage detection methods combined with segmentation.
• Post-processing algorithms localize electrode positions and wire centerlines from segmentation outputs, facilitating enhanced procedural guidance.

Clinical Implications

This multi-task deep learning framework can improve the accuracy and efficiency of catheter localization during minimally invasive heart surgeries, enhancing procedural guidance and potentially enabling robotic assistance. Real-time detection and segmentation allow for better integration with 3D anatomical overlays and motion compensation, which may reduce procedure times and improve patient outcomes.

Conclusion

The proposed multi-task learning model with dynamic resource prioritization demonstrates significant advancements in catheter detection and segmentation in X-ray images, offering a robust and efficient tool for clinical applications in minimally invasive cardiac procedures.

References

1. Wang et al. 2021 -- Multi-Task Learning for Catheter Detection in X-ray Images
2. Zhou et al. 2019 -- Attention Modules in Medical Image Analysis
3. Smith et al. 2020 -- Dynamic Task Prioritization in Deep Learning