Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy

2024-11-14 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Medical Devices & Health Technology · Depth: Expert, extended

Summary

A new multi-threaded deep learning pipeline has been developed for real-time catheter and guidewire tip tracking in fluoroscopic images, crucial for autonomous mechanical thrombectomy (MT) navigation. The pipeline integrates frame reading, preprocessing, inference, and post-processing, utilizing U-Net, U-Net+Transformer, and SegFormer segmentation models. Evaluated on the CathAction dataset and various in vitro and in vivo fluoroscopic data, the two-class SegFormer model achieved a mean absolute error of 4.44 mm on moderate complexity fluoroscopic video, outperforming other models. The system also surpassed existing CathAction benchmarks by up to +5% in Dice scores for three-segmentation, demonstrating robust performance under challenging imaging conditions like low contrast, noise, and device occlusion, despite in vivo MAE values remaining above sub-millimeter clinical targets.

Key takeaway

For Computer Vision Engineers developing autonomous endovascular navigation systems, this research highlights the effectiveness of a multi-threaded, SegFormer-based pipeline for real-time catheter tip tracking in fluoroscopy. You should prioritize two-class segmentation for optimal speed and accuracy, and integrate robust post-processing techniques like skeletonization and multi-point sampling to enhance stability under challenging clinical conditions, even if sub-millimeter precision requires further domain adaptation.

Key insights

A multi-threaded deep learning pipeline enables robust, real-time catheter tip tracking in fluoroscopy for autonomous navigation.

Principles

• Multi-threading improves throughput and minimizes latency.
• Two-class segmentation offers better speed and accuracy than three-class.
• Transformer models enhance robustness in complex backgrounds.

Method

The method uses a four-stage asynchronous pipeline: frame reading, preprocessing, deep learning segmentation (U-Net, U-Net+Transformer, SegFormer), and post-processing with skeletonization and multi-point sampling for tip localization.

In practice

• Implement multi-threaded pipelines for real-time medical image processing.
• Prioritize two-class segmentation for speed and accuracy in tip tracking.
• Consider SegFormer for robust tracking in complex clinical fluoroscopy.

Topics

• Catheter Tip Tracking
• Fluoroscopy
• Mechanical Thrombectomy
• Deep Learning Segmentation
• Transformer Architectures

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.