183 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
This study proposed a deep learning-based tracking method for ultrasound (US)
image-guided radiation therapy. The proposed cascade deep learning model is
composed of an attention network, a mask region-based convolutional neural
network (mask R-CNN), and a long short-term memory (LSTM) network. The
attention network learns a mapping from a US image to a suspected area of
landmark motion in order to reduce the search region. The mask R-CNN then
produces multiple region-of-interest (ROI) proposals in the reduced region and
identifies the proposed landmark via three network heads: bounding box
regression, proposal classification, and landmark segmentation. The LSTM
network models the temporal relationship among the successive image frames for
bounding box regression and proposal classification. To consolidate the final
proposal, a selection method is designed according to the similarities between
sequential frames. The proposed method was tested on the liver US tracking
datasets used in the Medical Image Computing and Computer Assisted
Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by
three experienced observers to obtain their mean positions. Five-fold
cross-validation on the 24 given US sequences with ground truths shows that the
mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all
landmarks are within 2 mm. We further tested the proposed model on 69 landmarks
from the testing dataset that has a similar image pattern to the training
pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental
results have demonstrated the feasibility and accuracy of our proposed method
in tracking liver anatomic landmarks using US images, providing a potential
solution for real-time liver tracking for active motion management during
radiation therapy
Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy
CNN-based real-time 2D-3D deformable registration from a single X-ray projection
Purpose: The purpose of this paper is to present a method for real-time 2D-3D
non-rigid registration using a single fluoroscopic image. Such a method can
find applications in surgery, interventional radiology and radiotherapy. By
estimating a three-dimensional displacement field from a 2D X-ray image,
anatomical structures segmented in the preoperative scan can be projected onto
the 2D image, thus providing a mixed reality view. Methods: A dataset composed
of displacement fields and 2D projections of the anatomy is generated from the
preoperative scan. From this dataset, a neural network is trained to recover
the unknown 3D displacement field from a single projection image. Results: Our
method is validated on lung 4D CT data at different stages of the lung
deformation. The training is performed on a 3D CT using random (non
domain-specific) diffeomorphic deformations, to which perturbations mimicking
the pose uncertainty are added. The model achieves a mean TRE over a series of
landmarks ranging from 2.3 to 5.5 mm depending on the amplitude of deformation.
Conclusion: In this paper, a CNN-based method for real-time 2D-3D non-rigid
registration is presented. This method is able to cope with pose estimation
uncertainties, making it applicable to actual clinical scenarios, such as lung
surgery, where the C-arm pose is planned before the intervention
Impact of Soft Tissue Heterogeneity on Augmented Reality for Liver Surgery
International audienceThis paper presents a method for real-time augmented reality of internal liver structures during minimally invasive hepatic surgery. Vessels and tumors computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Compared to current methods, our method is able to locate the in-depth positions of the tumors based on partial three-dimensional liver tissue motion using a real-time biomechanical model. This model permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Experimentations conducted on phantom liver permits to measure the accuracy of the augmentation while real-time augmentation on in vivo human liver during real surgery shows the benefits of such an approach for minimally invasive surgery
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion
Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life.
In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging.
Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets.
Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging
Interactive Multigrid Refinement for Deformable Image Registration
Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable registration methods. This paper focuses on how to revise registration results interactively for deformable image registration. We can manually revise the transformed image locally in a hierarchical multigrid manner to make the transformed image register well with the reference image. The proposed method is based on multilevel B-spline to interactively revise the deformable transformation in the overlapping region between the reference image and the transformed image. The resulting deformation controls the shape of the transformed image and produces a nice registration or improves the registration results of other registration methods. Experimental results in clinical medical images for adaptive radiotherapy demonstrated the effectiveness of the proposed method
Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery
Minimally invasive surgery is playing an increasingly important role for patient
care. Whilst its direct patient benefit in terms of reduced trauma,
improved recovery and shortened hospitalisation has been well established,
there is a sustained need for improved training of the existing procedures
and the development of new smart instruments to tackle the issue of visualisation,
ergonomic control, haptic and tactile feedback. For endoscopic
intervention, the small field of view in the presence of a complex anatomy
can easily introduce disorientation to the operator as the tortuous access
pathway is not always easy to predict and control with standard endoscopes.
Effective training through simulation devices, based on either virtual reality
or mixed-reality simulators, can help to improve the spatial awareness,
consistency and safety of these procedures.
This thesis examines the use of endoscopic videos for both simulation
and navigation purposes. More specifically, it addresses the challenging
problem of how to build high-fidelity subject-specific simulation environments
for improved training and skills assessment. Issues related to mesh
parameterisation and texture blending are investigated. With the maturity
of computer vision in terms of both 3D shape reconstruction and localisation
and mapping, vision-based techniques have enjoyed significant interest
in recent years for surgical navigation. The thesis also tackles the problem
of how to use vision-based techniques for providing a detailed 3D map and
dynamically expanded field of view to improve spatial awareness and avoid
operator disorientation. The key advantage of this approach is that it does
not require additional hardware, and thus introduces minimal interference
to the existing surgical workflow. The derived 3D map can be effectively
integrated with pre-operative data, allowing both global and local 3D navigation
by taking into account tissue structural and appearance changes.
Both simulation and laboratory-based experiments are conducted throughout
this research to assess the practical value of the method proposed
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