Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures
- URL: http://arxiv.org/abs/2507.00051v1
- Date: Wed, 25 Jun 2025 02:34:00 GMT
- Title: Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures
- Authors: Tianliang Yao, Zhiqiang Pei, Yong Li, Yixuan Yuan, Peng Qi,
- Abstract summary: This paper focuses on guidewire tip tracking tasks during image-guided therapy for cardiovascular diseases.<n>A novel tracking framework based on a Siamese network with dual attention mechanisms combines self- and cross-attention strategies for robust tip tracking.<n>The framework maintains an average processing speed of 57.2 frames per second, meeting the temporal demands of endovascular imaging.
- Score: 27.037820619664654
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An ever-growing incorporation of AI solutions into clinical practices enhances the efficiency and effectiveness of healthcare services. This paper focuses on guidewire tip tracking tasks during image-guided therapy for cardiovascular diseases, aiding physicians in improving diagnostic and therapeutic quality. A novel tracking framework based on a Siamese network with dual attention mechanisms combines self- and cross-attention strategies for robust guidewire tip tracking. This design handles visual ambiguities, tissue deformations, and imaging artifacts through enhanced spatial-temporal feature learning. Validation occurred on 3 randomly selected clinical digital subtraction angiography (DSA) sequences from a dataset of 15 sequences, covering multiple interventional scenarios. The results indicate a mean localization error of 0.421 $\pm$ 0.138 mm, with a maximum error of 1.736 mm, and a mean Intersection over Union (IoU) of 0.782. The framework maintains an average processing speed of 57.2 frames per second, meeting the temporal demands of endovascular imaging. Further validations with robotic platforms for automating diagnostics and therapies in clinical routines yielded tracking errors of 0.708 $\pm$ 0.695 mm and 0.148 $\pm$ 0.057 mm in two distinct experimental scenarios.
Related papers
- Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification [0.0]
Colon cancer detection is crucial for increasing patient survival rates.<n> colonoscopy is dependent on obtaining adequate and high-quality endoscopic images.<n>Few-Shot Learning architecture enables our model to rapidly adapt to unseen fine-grained endoscopic image patterns.<n>Our model demonstrated superior performance, achieving an accuracy of 90.1%, precision of 0.845, recall of 0.942, and an F1 score of 0.891.
arXiv Detail & Related papers (2025-05-30T16:54:51Z) - Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms [2.1113382954657594]
This study is the first to validate an autonomous robotic system for U.S. scanning of the femoral artery on a diverse set of patient-specific phantoms.<n>We introduce a video-based deep learning US segmentation network tailored for vascular imaging, enabling improved 3D arterial reconstruction.
arXiv Detail & Related papers (2025-03-09T22:20:25Z) - DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images [8.359851428921386]
The paper proposes an adversarial learning-based dynamic architecture adjustment approach that integrates hierarchical U-shaped encoder-decoder, residual blocks, attention mechanisms, and ASPP modules.<n> Experimental results demonstrate a Dice coefficient of 0.6802, IoU of 0.5602, Recall of 0.766, Precision of 0.6525, and Accuracy of 0.9955, effectively addressing the challenges in fundus image hemorrhage segmentation.
arXiv Detail & Related papers (2025-02-13T12:11:58Z) - Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life.
This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events.
We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
arXiv Detail & Related papers (2024-10-18T12:37:51Z) - Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images [0.0]
This paper introduces a novel, lightweight convolutional neural network architecture tailored for automated exudate detection.
We have incorporated domain-specific data augmentations to enhance the model's generalizability.
Our model achieves an impressive F1 score of 90%, demonstrating its efficacy in the early detection of diabetic retinopathy through fundus imaging.
arXiv Detail & Related papers (2024-08-13T10:13:33Z) - Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging [39.597735935731386]
A class-aware cartilage bone segmentation network with geometry-constraint post-processing is presented to capture patient-specific rib skeletons.
A dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients.
Results demonstrate that the proposed graph-based registration method can robustly and precisely map the path from CT template to individual patients.
arXiv Detail & Related papers (2024-06-06T14:15:15Z) - Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning [45.5123007404575]
The US examination for thoracic application is still challenging due to the acoustic shadow cast by the subcutaneous rib cage.
We present a reinforcement learning approach for planning scanning paths between ribs to monitor changes in lesions on internal organs.
Experiments have been carried out on unseen CTs with randomly defined single or multiple scanning targets.
arXiv Detail & Related papers (2024-04-15T16:52:53Z) - Real-time guidewire tracking and segmentation in intraoperative x-ray [52.51797358201872]
We propose a two-stage deep learning framework for real-time guidewire segmentation and tracking.
In the first stage, a Yolov5 detector is trained, using the original X-ray images as well as synthetic ones, to output the bounding boxes of possible target guidewires.
In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box.
arXiv Detail & Related papers (2024-04-12T20:39:19Z) - Longitudinal Multimodal Transformer Integrating Imaging and Latent
Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification [4.002181247287472]
We propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from EHRs for solitary pulmonary nodule (SPN) classification.
We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans.
arXiv Detail & Related papers (2023-04-06T03:03:07Z) - Systematic Clinical Evaluation of A Deep Learning Method for Medical
Image Segmentation: Radiosurgery Application [48.89674088331313]
We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task.
Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
arXiv Detail & Related papers (2021-08-21T16:15:40Z) - Automated Detection of Coronary Artery Stenosis in X-ray Angiography
using Deep Neural Networks [0.0]
We propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images.
We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively.
arXiv Detail & Related papers (2021-03-04T11:45:54Z) - Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals.
Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts.
We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
arXiv Detail & Related papers (2020-06-18T09:49:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.