A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

Tonutti, Michele; Gras, Gauthier; Yang, Guang‐Zhong

doi:10.1016/j.artmed.2017.07.004

Cited by 66 publications

(45 citation statements)

References 58 publications

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“…Hambli et al [10] have used a fully connected net to predict the velocity and angle of a tennis ball after hitting a racket. A similar approach has been adopted by Tonutti et al [29] for predicting the movement of a tumor during brain surgery, but the displacement of the healthy tissue is not considered. Rechowicz et al [23] estimate the displacement of a patient's rib cage, but only predict the displacement of surface nodes.…”

Section: Related Workmentioning

confidence: 99%

Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks

et al. 2019

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Purpose: In surgical navigation, pre-operative organ models are presented to surgeons during the intervention to help them in efficiently finding their target. In the case of soft tissue, these models need to be deformed and adapted to the current situation by using intra-operative sensor data. A promising method to realize this are real-time capable biomechanical models.Methods: We train a fully convolutional neural network to estimate a displacement field of all points inside an organ when given only the displacement of a part of the organ's surface. The network trains on entirely synthetic data of random organ-like meshes, which allows us to generate much more data than is otherwise available. The input and output data is discretized into a regular grid, allowing us to fully utilize the capabilities of convolutional operators and to train and infer in a highly parallelized manner. Results: The system is evaluated on in-silico liver models, phantom liver data and human in-vivo breathing data. We test the performance with varying material parameters, organ shapes and amount of visible surface. Even though the network is only trained on synthetic data, it adapts well to the various cases and gives a good estimation of the internal organ displacement. The inference runs at over 50 frames per second.Conclusions: We present a novel method for training a data-driven, real-time capable deformation model. The accuracy is comparable to other registration methods, it adapts very well to previously unseen organs and does not need to be re-trained for every patient. The high inferring speed makes this method useful for many applications such as surgical navigation and real-time simulation.

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Section: Related Workmentioning

confidence: 99%

Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks

et al. 2019

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“…Ho et al showed that the visualization and computing of deformations in real time are essential in surgical simulation of soft tissues [6]. In the field of image-guided surgeries, the estimation of soft-tissue deformations in real time is also one of the most important challenges [7]. Note that in image-guided surgery systems, computation time is commonly expensive due to online data acquisition from medical imaging and additional data processing.…”

Section: Introductionmentioning

confidence: 99%

“…Note that in image-guided surgery systems, computation time is commonly expensive due to online data acquisition from medical imaging and additional data processing. In fact, most simulation systems with soft-tissue deformations hardly satisfy real-time requirements [8], and they cannot both correctly compute soft-tissue deformations and effectively achieve real-time computation speeds [7]. However, despite this hard constraint, numerous strategies have been developed for improving both computation speed and accuracy of soft-tissue simulation systems.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Real-Time Medical Simulations with Soft-Tissue Deformation: Computational Approaches, Interaction Devices, System Architectures, and Clinical Validations

Nguyen

Tho

Dao

2020

Applied Bionics and Biomechanics

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Simulating deformations of soft tissues is a complex engineering task, and it is even more difficult when facing the constraint between computation speed and system accuracy. However, literature lacks of a holistic review of all necessary aspects (computational approaches, interaction devices, system architectures, and clinical validations) for developing an effective system of soft-tissue simulations. This paper summarizes and analyses recent achievements of resolving these issues to estimate general trends and weakness for future developments. A systematic review process was conducted using the PRISMA protocol with three reliable scientific search engines (ScienceDirect, PubMed, and IEEE). Fifty-five relevant papers were finally selected and included into the review process, and a quality assessment procedure was also performed on them. The computational approaches were categorized into mesh, meshfree, and hybrid approaches. The interaction devices concerned about combination between virtual surgical instruments and force-feedback devices, 3D scanners, biomechanical sensors, human interface devices, 3D viewers, and 2D/3D optical cameras. System architectures were analysed based on the concepts of system execution schemes and system frameworks. In particular, system execution schemes included distribution-based, multithread-based, and multimodel-based executions. System frameworks are grouped into the input and output interaction frameworks, the graphic interaction frameworks, the modelling frameworks, and the hybrid frameworks. Clinical validation procedures are ordered as three levels: geometrical validation, model behavior validation, and user acceptability/safety validation. The present review paper provides useful information to characterize how real-time medical simulation systems with soft-tissue deformations have been developed. By clearly analysing advantages and drawbacks in each system development aspect, this review can be used as a reference guideline for developing systems of soft-tissue simulations.

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“…Most of the current FE models of the brain are based on general anatomical structures, which lack geometrical details of the brain [7]. Although the general brain models could provide important information for quantifying the brain responses during the interventional process, patient-specific models are still needed for clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image-Based Modeling for Neurosurgical Interventions

Li¹,

Lai²,

Zhang³

et al. 2019

Molecular &Amp; Cellular Biomechanics

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Surgeries such as implantation of deep brain stimulation devices require accurate placement of devices within the brain. Because placement affects performance, image guidance and robotic assistance techniques have been widely adopted. These methods require accurate prediction of brain deformation during and following implantation. In this study, a magnetic resonance (MR) image-based finite element (FE) model was proposed by using a coupled Eulerian-Lagrangian method. Anatomical accuracy was achieved by mapping image voxels directly to the volumetric mesh space. The potential utility was demonstrated by evaluating the effect of different surgical approaches on the deformation of the corpus callosum (CC) region. The results showed that the maximum displacement of the corpus callosum increase with an increase of interventional angle with respect to the midline. The maximum displacement of the corpus callosum for different interventional locations was predicted, which is related to the brain curvature and the distance between the interventional area and corpus callosum (CC). The estimated displacement magnitude of the CC region followed those obtained from clinical observations. The proposed method provided an automatic pipeline for generating realistic computational models for interventional surgery. Results also demonstrated the potential of constructing patient-specific models for imageguided, robotic neurological surgery.

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A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

Cited by 66 publications

References 58 publications

Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks

Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks

A Systematic Review of Real-Time Medical Simulations with Soft-Tissue Deformation: Computational Approaches, Interaction Devices, System Architectures, and Clinical Validations

Magnetic Resonance Image-Based Modeling for Neurosurgical Interventions

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