10 research outputs found
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
Augmenting X-ray imaging with 3D roadmap to improve guidance is a common
strategy. Such approaches benefit from automated analysis of the X-ray images,
such as the automatic detection and tracking of instruments. In this paper, we
propose a real-time method to segment the catheter and guidewire in 2D X-ray
fluoroscopic sequences. The method is based on deep convolutional neural
networks. The network takes as input the current image and the three previous
ones, and segments the catheter and guidewire in the current image.
Subsequently, a centerline model of the catheter is constructed from the
segmented image. A small set of annotated data combined with data augmentation
is used to train the network. We trained the method on images from 182 X-ray
sequences from 23 different interventions. On a testing set with images of 55
X-ray sequences from 5 other interventions, a median centerline distance error
of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The
segmentation of the instruments in 2D X-ray sequences is performed in a
real-time fully-automatic manner.Comment: Accepted to MICCAI 201
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.Comment: Submitted to ICRA 201
End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention
Accurate real-time catheter segmentation is an important pre-requisite for
robot-assisted endovascular intervention. Most of the existing learning-based
methods for catheter segmentation and tracking are only trained on small-scale
datasets or synthetic data due to the difficulties of ground-truth annotation.
Furthermore, the temporal continuity in intraoperative imaging sequences is not
fully utilised. In this paper, we present FW-Net, an end-to-end and real-time
deep learning framework for endovascular intervention. The proposed FW-Net has
three modules: a segmentation network with encoder-decoder architecture, a flow
network to extract optical flow information, and a novel flow-guided warping
function to learn the frame-to-frame temporal continuity. We show that by
effectively learning temporal continuity, the network can successfully segment
and track the catheters in real-time sequences using only raw ground-truth for
training. Detailed validation results confirm that our FW-Net outperforms
state-of-the-art techniques while achieving real-time performance.Comment: ICRA 202
Improved Image Guidance in TACE Procedures
Purpose of the work in this thesis is to improve the image guidance in TACE procedures.
More specifically, we intend to develop and evaluate technology that permits dynamic roadmapping based on a 3D model of the liver vasculature
Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers
International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of , for the 3D mean distance at the segment of mm and an average 3D tip error of . For the real data-set,we obtained an average 3D Hausdorff distance of , a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of . These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions
Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions
Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter