Feature Survey

54 March/April 2007 Published by the IEEE Computer Society 0272-1716/07/$25.00 © 2007 IEEE

Throughout medical history, the trainingparadigm for surgeons has not changed

substantially. Traditionally, surgical training has fol-lowed the apprenticeship model: Novice surgeonsreceive their training over time in small groups of peersand superiors in the course of patient care. The oper-ating room (OR) and the patient comprise the mostcommon, the most readily available, and often the onlysetting where hands-on training takes place. Novice sur-geons acquire skills by observing experienced surgeonsin action and then progressively performing addition-

al surgical procedures under vary-ing degrees of supervision as theirtraining advances and skill levelsincrease. This so-called “see one, doone, teach one” paradigm hasproved reasonably effective formore than 2,500 years.

Recently, however, experts, physi-cians, and the public are examiningthis training model and questioningits efficiency. According to “To Err isHuman,” a 1999 report from theInstitute of Medicine of the Nation-al Academy of Sciences, more peo-ple die from medical mistakes eachyear than from highway accidents,breast cancer, or AIDS. In additionto this devastating human cost, the

financial burden is significant. Among the main reasonscited for this situation are the inexperience of beginners,as well as the inexperience of experts with new tech-niques and rare medical situations. One of the majorshortfalls identified in the report is medical educationand training.

In particular, minimally invasive surgery (MIS) is arevolutionary surgical technique that poses an immedi-ate need for improved training methods. Physicians haveused MIS in various procedures since the early 1960s.

This technology involves a small video camera and a fewcustomized surgical instruments.1 The surgeon insertsthe camera and instruments into the body through smallskin incisions or natural orifices to explore internal cav-ities without making large openings. For patients, MIS’smajor advantages over conventional surgery are a short-er hospital stay, a quicker return to activities, and lesspain and scarring. Some common MIS procedures arelaparoscopic cholecystectomy (gallbladder removal),appendectomy, and hernia repair. Using minimally inva-sive techniques is a trend in other procedures as well.We can predict that as instruments get smaller and thuseasier for surgeons to handle, new minimally invasivetechniques will develop.

In spite of the advantages of MIS over traditionalsurgery, surgeons are still handicapped by the currenttechnology’s limitations, which pose four problems inthe OR:

■ Visualization of internal organs achieved with a wide-angle camera is monoscopic and limited by the cam-era’s field of view.

■ Hand-eye coordination is difficult because surgeonsmust move the tool around a pivot point, thus invert-ing the direction of movement inside and outside thebody. Moreover, the location of the displayed imageis not the actual manipulation site.

■ Surgeons receive limited haptic (tactile sensing andforce feedback) cues because they must interact withinternal organs by means of surgical instrumentsattached to long, thin tubes.

■ The instruments rotate about a fixed entrance point,making it impossible for the surgeon to perform directtranslational movements while interacting withorgans.

Although the importance of MIS training is widelyacknowledged, there is no consensus on the most effective training method. OR time is an expensive and

Simulation-based training usingVR techniques is a promisingalternative to traditionaltraining in minimally invasivesurgery (MIS). Simulators letthe trainee touch, feel, andmanipulate virtual tissues andorgans through the samesurgical tool handles used inactual MIS while viewingimages of tool-tissueinteractions on a monitor as inreal laparoscopic procedures.

Cagatay Basdogan and Mert SedefKoc University

Matthias HardersETH Zurich

Stefan WesargFraunhofer Institute for Computer Graphics

VR-BasedSimulators forTraining inMinimally InvasiveSurgery

limited resource to use for training surgeons. Many lead-ing academic institutions in the United States andEurope have established training centers with facilitiesfor practicing surgical techniques on both inanimate andanimate models. Box trainers, for instance, are inani-mate models equipped with real surgical instruments,endoscopic cameras, and plastic tissue models. Theyprovide the trainee with an environment similar to actu-al surgery settings. However, simulated surgical proce-dures are usually poor imitations of actual ones. It is noteasy to customize these training systems to the trainee’sneeds. Moreover, it is not easy to measure the trainee’sperformance with these systems.

Currently, the most realistic training model availableis animals. This model is dynamic and approaches realoperative conditions. Animal tissues, although notalways of the same consistency as human tissues,respond similarly to applied forces. Using animals fortraining, however, is expensive and controversial. Itrequires expensive, dedicated facilities, including careand housing of the animals. Only a few trainees (oftenonly one) can practice on the same animal and for onlya limited number of times. (The expensive training ses-sion generally ends with euthanizing the animal.) Addi-tionally, animal anatomies are different from humananatomies, and ethical issues surround the use of ani-mals for training. Finally, with animal models, quantita-tive measurement of a trainee’s performance is notstraightforward, and evaluation (performed by theinstructor) is often subjective.

With either inanimate or animate models, conven-tional MIS training methodologies suffer from the samemain drawbacks: the need for an instructor or supervi-sor, nonstandard feedback methods, and subjective per-formance evaluation methods. Hence, new trainingapproaches and devices to reduce the risks and con-straints of surgical procedures are necessary. To meetthis need, VR-based surgical simulators that give the sur-geon visual and haptic cues promise to bepowerful aids for training medical person-nel and monitoring their performance.1

Computer-based simulation can revolution-ize medical education and augment trainingby quantifying performance and progress,standardizing training regimens indepen-dent of patient population, and exposingtrainees to unusual cases.2-4 Integrating VR-based simulators in medical training wouldresult in better-trained physicians, reducingthe likelihood of error and improving patientoutcome. Figure 1 shows a typical VR-basedsurgical simulator.

MIS simulator developmentDeveloping a VR-based MIS simulator

requires expertise in systems engineering,materials engineering, robotics engineering,computer science, biomedical engineering,and medicine. As Figure 2 shows, simulatordevelopment involves six steps. First, thedevelopers use segmentation and recon-struction techniques of computer vision and

computer graphics to generate 3D anatomical modelsof organs from medical images. Second, they measureand characterize the material properties of soft tissuesand integrate these properties in organ-force models.Next, they develop collision detection and responsetechniques to simulate the real-time interactions of sim-ulated surgical instruments and manipulated organs.Then, they integrate the simulator’s hardware and soft-ware components to form a complete system. Finally,they validate the system and measure training transferthrough user studies.

Anatomical model and training-scenegeneration

Medical applications use various imaging modalities.Anatomical imaging techniques include computedtomography (CT), magnetic resonance imaging (MRI),and ultrasound. Functional techniques include single

IEEE Computer Graphics and Applications 55

1 Componentsof a typicalminimally invasive surgerysimulator (theSimbionix LAP Mentorlaparoscopicsurgery simulator)include a visualdisplay andsurgical instrumentsfitted withhaptic devicesfor force feed-back (photocourtesy ofSimbionix).

Haptic interface

Visualinterface

Anatomical modelgeneration

Surgicalsimulator

Tissuemeasurement andcharacterization

Physics-basedmodeling

Surgical tool–soft tissue

interactions

System integration

Assessment,validation and

training transfer

2 Simulator development steps for minimally invasive surgery.

photon emission computed tomography (Spect),positron emission tomography (PET), and functionalMRI. For generating anatomical models, anatomicaltechniques play the key role—mainly CT and MRI, whichprovide sufficient resolution. Today’s CT scanners, whichintegrate 64 detector rows, provide nearly isotropic vox-els of approximately 0.4 mm. MRI devices provide a spa-tial resolution of about 1 mm in each direction. Besidesdifferent spatial resolutions, the main differencebetween CT and MRI is their ability to distinguish differ-ent tissue types. CT makes it easy to see bone structures,whereas MRI provides superior soft tissue contrast. Inrecent years, the use of CT for virtual colonoscopy andbronchoscopy has gained importance. These techniquessupplement or even replace endoscopic procedures byemploying a patient model derived from CT data and dis-played with the help of volume-rendering techniqueswithout any preprocessing. Ultrasound, although wide-ly available and inexpensive, suffers from a lower imag-ing quality than CT and MRI and is therefore notappropriate for anatomical modeling.

The National Library of Medicine’s Visible Human(Man and Woman) data sets have become standardsources of medical image scans of the human body.Because of their high resolution and good image quali-ty, several projects have used them for highly accurateanatomical models.

To build 3D models of organs from image data, devel-opers of surgical simulators use either surface or volu-metric elements. Surface models represent the externalborder of the organs. Generating surface modelsrequires extraction of the structure’s outer surface, usingsegmentation algorithms that provide the outer con-tour. Figure 3 shows an example of the segmentationand generation of a 3D surface model.

The segmentation approaches often used for anatom-ical modeling are simple classification schemes such asthresholding and region growing. These techniquesextract isocontours that serve as input to the marchingcubes algorithm, which creates a polygonal representa-tion of the structure’s surface. Another way to extractorgan surfaces is to use active contour models, alsoknown as snakes. This technique obtains a contour byadjusting splines that fit the structure’s outer surface,using a physical description of the image data’s exter-nal and internal forces. Applying the contour found inone 2D slice to the next neighboring slice starts a con-tour that is gradually refined. This process, called

boundary tracking, delivers the organ’s 3D contour andthus its surface representation.

An advantage of generating surface models from CTand MRI scans is the reduction in data size. Also, we candisplay the triangulated meshes created by this methodusing the hardware acceleration available from moderngraphics boards.

An alternative to surface modeling and surface datavisualization is direct volume rendering. Ray casting, aclassical volume-rendering technique, provides high-resolution visualization but is rather slow.

Several commercial and free software packages areavailable for 3D reconstruction from medical images,but most are semiautomated and often require labor-and time-intensive segmentation. Amira (http://www.amiravis.com), Analyze (http://www.mayo.edu/bir/Software/Analyze/Analyze.html), IDL (http://www.ittvis.com/idl/), Image-Pro (http://www.mediacy.com/), and MEDx (http://medx.sensor.com/products/medx/index.html) are commercial packages for med-ical and other scientific image data visualization andmanipulation, anatomical structure extraction, and sur-face and tetrahedral model generation. The Visualiza-tion Toolkit (http://www.vtk.org) is a free, open-sourceimage data visualization package with contour extrac-tion and mesh generation algorithms. Another free vol-ume visualization system is VolVis (http://www.cs.sunysb.edu/~vislab/volvis_home.html). Mesh gener-ation and manipulation packages developed at academ-ic institutions include TetGen (http://tetgen.berlios.de/) and SUMAA3d (http://www-unix.mcs.anl.gov/sumaa3d).

For a realistic visualization of reconstructed organmodels, system developers map textures to the models’surfaces. This process involves generating realistic tex-tures and obtaining appropriate texture coordinates.The most basic method of creating organ textures isdirect texture painting, which usually requires a med-ical illustrator to manually draw the texture with appro-priate tools. Another way to obtain textures for medicalimages is to map real volumetric data to surfaces. K.D.Reinig et al. use the Visible Human data set to obtainorgan textures.5 Recently, Paget, Harders, and Szekelyintroduced a fully automatic framework for generatingvariable textures.6 The first step in their approach isacquiring in vivo images to form a database. Next, a tex-ture synthesis step creates tileable variable textures fromthe in vivo images. The final step is mapping the textureto the 3D mesh geometry.

Soft tissue measurement and characterizationA core component of a VR-based surgical simulation

and training system is realistic organ-force models. Real-istic organ-force models are virtual representations ofsoft tissues that display accurate displacement and forceresponse. To develop these models, we must measureand characterize the material properties of organs in liv-ing condition and in their native locations. Models withincorrect material properties will result in adverse train-ing effects. Measuring the material properties of softorgan tissues is highly challenging. Soft tissues exhibitcomplex, nonlinear, anisotropic, nonhomogeneous

Feature Survey

56 March/April 2007

3 A set of 2D medical images of the abdomen is segmented via filteringtechniques for identifying different tissue regions, lesions, and pathologies(a). Segmented contours in each image are combined to create a 3D sur-face model of the organ—the liver in this example (b). Texture mappingover the surface gives the model a more realistic appearance (c).

Liver

(a) (b) (c)

behavior. Moreover, the tissues are layered, and eachlayer consists of different materials in varying combina-tions. Because of this nonhomogeneity, soft tissues haveboth coordinate- and direction-dependent properties.Time- and rate-dependent behavior caused by viscoelas-ticity is also common.

Various methods of measuring material properties oforgan tissues have appeared in the literature. We cate-gorize these methods in terms of the measurement siteand the degree of tissue damage that occurs during mea-surement. The two types of measurement sites are exvivo and in vivo. In the past, most tissue experimentswere ex vivo studies.7-9 For ex vivo measurements, thetissue’s biological functioning has ceased; in otherwords, the tissue to be measured is dead. Ex vivo mea-surements can take place within the body (in situ) oroutside the body (in vitro). For invitro measurements, researchersuse standard materials-testingmethods (tension or compressiontests) under well-defined boundaryconditions. Typically, they transfertissue samples in a chemical solutionto a laboratory for measurements.Because they carefully decide onsample geometry and experimentalconditions in advance, they can eas-ily obtain stress and strain valuesfrom the measurement data. How-ever, dead organ and muscle tissues typically stiffen withtime, leading to changes in mechanical properties, sothe results of in vitro measurements can be misleading.Therefore, recent research has focused on in vivo, in situmeasurement of soft tissues’ mechanical properties.

Soft-tissue measurement methods incur three levelsof tissue damage: invasive, noninvasive, and minimal-ly invasive. In invasive methods, measurement instru-ments enter the body through a puncture or an incision.Because large openings allow easy insertion of testapparatus into the body, scientists have performedmany measurements invasively. However, the experi-mental devices and procedures used for invasive mea-surements typically don’t match the actual surgicaldevices and procedures used during MIS. In addition,the invasive approach is unacceptable for conductinghuman experiments.

In contrast, noninvasive tissue measurements requireno incisions. Noninvasive approaches include CT, MRI,and ultrasound. Most of these approaches can measureonly linear material properties, but soft organ tissuesexhibit nonlinear material characteristics as well.

Minimally invasive methods require small incisions,causing much less tissue damage than the invasivemethod does. A few research groups have recently con-ducted minimally invasive animal and human experi-ments to characterize nonlinear and time-dependentmaterial properties of soft tissues. A challenge of thisapproach is characterizing the measured properties.Determining unknown material properties from themeasured system response requires formulating aninverse solution. For this purpose, scientists typicallyconstruct a finite-element model of the soft tissue and

use it with an optimization method to iteratively matchthe experimental data to the numerical solution.7,9

Physics-based modelingDeveloping realistic organ-force models for simulating

soft-tissue behavior requires a system that reflects stableforces to the user, displays realistic smooth deformationsin real time, and handles various boundary conditionsand constraints.1 The material properties and structureof organ tissues mentioned earlier make developing real-time, realistic organ-force models challenging. In addi-tion, surgical-tool and soft-tissue interactions causedynamic effects and contact between organs, which aredifficult to simulate in real time. Furthermore, simulat-ing surgical operations such as cutting and coagulationrequires updating the organ’s geometric database fre-

quently and can cause force singular-ities in the physics-based model atthe boundaries.

We classify current physics-basedapproaches for developing organ-force models as mesh free and meshbased. Mesh-free methods use pointclouds (vertices) only for deformationand force computations and make noassumptions about the underlyinggeometry. Most mesh-based methodsconsider the deformable object a con-tinuum and are generally more accu-

rate than mesh-free techniques. However, mesh-freetechniques provide a better solution to topological changesencountered in simulating surgical cutting and tearing. Inaddition, they are computationally less expensive and eas-ier to implement than mesh-based methods.

Mesh-based methods. One of the most widely usedmesh-based methods is the finite-element method.10-14

FEM solves the deformation problem by considering theorgan a continuous body that is trying to minimize itspotential energy under the influence of external forces.To implement this method, we divide the geometricmodel of an organ into surface or volumetric elements,formulate each element’s properties, and combine theelements to compute the organ’s deformation statesunder the forces applied by the surgical instruments.1

A major advantage of FEM is that it uses continuummechanics and has a solid mathematical foundation. Onthe basis of the partial differential equations and theconstitutive relation used, FEM can accurately approx-imate static and dynamic deformations of an object withlinear and nonlinear material properties.13 Anotheradvantage is that FEM requires only a few material para-meters to describe a physical system’s response.

However, FEM also has some drawbacks. It has a heavycomputational load, and its computational complexityusually increases quadratically with the underlyingmesh’s quality. Moreover, while simulating a proceduresuch as cutting, in which the object’s topology is modi-fied, we must recalculate and reassemble element massand stiffness matrices, which is computationally inten-sive. Precomputation and condensation have been sug-gested as remedies to these problems.11

IEEE Computer Graphics and Applications 57

Most mesh-based methods

consider the deformable

object a continuum and are

generally more accurate

than mesh-free techniques.

Another mesh-based method based on continuummechanics is the boundary element method. BEM dis-cretizes an object’s surface or boundary into elementsand patches and relies on surface integral equations tocalculate displacements at the boundary.15 On theassumption of linear elasticity, BEM computes smalldeformations accurately. Extending this approach tolarge deformation analysis is not straightforward.Another drawback is that direct solution of BEM is com-putationally too expensive to execute in real time. Yet,precomputation and superposition make it possible toexecute a linear deformable model at haptic updaterates.15 BEM can model changes in topology resultingfrom procedures such as cutting by using iterativesolvers that update precomputed data to approximatethe modified topology.

The long-element method is another mesh-basedapproach. It is based on Hooke’s law, Pascal’s principle,and volume conservation as theboundary condition.16 LEM dis-cretizes the object into a set of two-dimensional long elements filledwith an incompressible fluid. Duringdeformation computation, these ele-ments reach equilibrium under theeffect of bulk variables includingpressure, density, volume, and stress.One advantage of LEM is that theparameters such as pressure, densi-ty, and volume are easy to identify.The elements filled with an incom-pressible fluid can represent nonhomogeneous materialproperties. Because the method intrinsically preservesvolume, it supports topological changes such as cutting.On the other hand, LEM produces accurate results forsmall deformations only. It yields inconsistent results forlarge deformations. The element deformations must bereevaluated when the object undergoes large deforma-tions, a bottleneck for real-time performance.

The tensor-mass model is also a mesh-basedapproach. Its methodology lies between the continuummechanics and particle-based approaches. Cotin,Delingette, and Ayache developed TMM as a continu-um model based on linear elasticity.17 The model dis-cretizes the object into tetrahedrons, and the tensors arestored at the edges of the tetrahedrons. Like particle-based approaches, the object’s mass is stored in thenodes of the tetrahedrons as lumped mass points. How-ever, unlike particle-based approaches, TMM computesdeformation and force through energy-based continu-um mechanics, and the computations are independentof mesh topology. One of TMM’s main advantages is thatthe model can handle topological modifications; hence,we can use it to simulate tissue cutting and tearing. Inaddition, TMM’s time complexity is linear and lowerthan that of the standard FEM approach. The initiallyproposed TMM approach could simulate small defor-mations only. Later, Picinbono, Delingette, and Ayacheextended TMM to simulate large deformations as well.18

Mesh-free methods. The mass-spring model, alsocalled the particle system approach, is a widely used

mesh-free method in surgical simulation.10,19 MSM mod-els the object as point masses connected to each otherwith springs and dampers.1 Each point mass is repre-sented by its own position, velocity, and accelerationand moves under the influence of inertial and dampingforces and the forces applied by the surgical instrument.This technique is relatively easy to implement becausethe motion equations need not be constructed explicit-ly. Hence, the technique’s computational complexityallows real-time simulation. However, the integrationof realistic tissue properties into particle models is nottrivial. In addition, the resulting physical behaviordepends on the point masses’ connectivity. The con-struction of an optimal spring network in 3D is a compli-cated process, and MSM can become oscillatory orunstable under certain conditions.

The point-associated finite-field (PAFF) approach,also called the finite-spheres method, is a newer mesh-

less FEM approach applied to surgi-cal simulation.20 This method, likeTMM, resides between the continu-um mechanics and particle-basedapproaches. Like MSM, it is a point-based approach, using only thenodes of a 3D object for the dis-placement and force calculations.PAFF approximates the displace-ment field by using nonzero func-tions over small spherical neighbor-hoods of nodes. Like FEM, this tech-nique uses a Galerkin formulation

to generate the discretized versions of the partial dif-ferential equations governing the deformable medium’sbehavior. PAFF supports simulation of large deforma-tions as well as topology modifications such as cutting.PAFF can also be used to simulate other proceduresinvolving particles, such as smoke generation duringcauterization. Although the technique’s brute-forceimplementation is computationally intensive, users cangenerate localized solutions in real time.20

Simulating tool-tissue interactionsSimulating interactions between surgical tools and

soft tissues involves graphical rendering of computer-generated models of surgical instruments, detecting col-lisions between instruments and deformable organmodels, and haptic rendering of the collision responsein the procedure to be simulated.1

We classify MIS tools on the basis of their functional-ity:

■ long, thin, straight tools for palpation, puncture, andinjection (for example, palpation probes and punc-ture and injection needles) and

■ articulated tools for grasping, pulling, clamping, cut-ting, and coagulating (biopsy and punch forceps,grasping forceps, hook scissors, and coagulationhooks).

For realistic visual display during simulations, we ren-der 3D graphical models of surgical tools in exact dimen-sions and shape using several polygons. But we typically

Feature Survey

58 March/April 2007

Mesh-free techniques

provide a better solution to

topological changes

encountered in simulating

surgical cutting and tearing.

assume that the models consist of a set of geo-metric primitives such as points or connectedline segments for fast detection of collisionsbetween instruments and organs in real time.Collision detection algorithms developed incomputer graphics cannot be used directly inrendering force interactions between instru-ments and organs. Nevertheless, to achieve real-time update rates, haptic rendering algorithmscan take advantage of computer graphics ren-dering techniques:

■ space partitioning (partitioning the space thatencloses an object into smaller subspaces forfaster detection of the first contact),

■ local search (searching only the neighboringprimitives for possible new contacts), and

■ hierarchical data structures (constructing hierarchi-cal links between primitives constituting the objectfor faster access to the contacted primitive).

In point-based haptic interactions of surgical instru-ments with organs, only the instrument’s end pointinteracts with virtual organs. Each time the user movesthe surgical instrument fitted with haptic devices inphysical space, the collision detection algorithm checkswhether the end point is inside the virtual organ. Thisapproach provides users with similar force feedback asthey would feel when exploring organs in real surgerysettings with the tip of an instrument only. However,actual MIS instruments have long, slender bodies, sopoint-based rendering methods are not sufficient to ren-der realistic tool-tissue interactions.

In ray-based haptic interaction models, the probe is afinite line segment whose orientation the detection algo-rithm takes into account while checking for collisionsbetween the line segment and the objects. This tech-nique has several advantages over point-based render-ing. In addition to displaying forces, users can feeltorques if they are using an appropriate haptic device,which is not possible with point-based approaches. Forexample, they can feel the coupling moments generat-ed by contact forces at the instrument tip and the forcesat a trocar’s pivot point. Second, users can detect sidecollisions between the simulated tool and 3D organmodels. Third, users can render multiple tissue layersby virtually extending the ray representing the simulat-ed surgical probe to detect collisions with an organ’sinternal layers. Finally, they can touch and feel multipleobjects simultaneously.

Once the algorithm detects contact between aninstrument and tissue, the tool-tissue interaction prob-lem centers on collision response. This involves a realis-tic graphical and haptic display of tissue behavioraccording to instrument type and the surgical task theuser chooses to perform. Tissue deformation is the mostgeneric collision response. Simulation of basic surgeryskills such as palpating, grasping, stretching, translo-cating, and clip applying mainly involves tissue defor-mation. Simulation of surgical cutting such astranssection, dissection, and coagulation fall into a dif-ferent category, in which tool-tissue interactions modi-

fy the geometry and the underlying model. Figure 4shows several of these simulated interactions.

Realistic graphical and haptic simulation of cutting isa requirement in any surgical simulator. Research in thisarea has focused mainly on the graphical display of cutand tissue separation, but some recent studies reportthe development of mechanistic models for displayingforces during cutting. Cutting approaches for graphicalsimulation include straightforward element deletion,mesh subdivision, and topology adaptation.

Cotin, Delingette, and Ayache have applied straight-forward deletion of mesh entities to remove the ele-ments contacted by a cutting tool.17 Unfortunately, thismethod leads to visual artifacts because it cannotapproximate the cutting path accurately. Achievingacceptable visual quality would require very high reso-lution meshes. Moreover, the method violates the phys-ical principle of mass conservation.

Mesh subdivision methods have produced better visu-al representations of incisions. Bielser et al. discuss theuse of a state machine to keep track of incisions in tetra-hedral meshes.21 All the described mesh subdivisionapproaches considerably increase the element count.Moreover, introducing new mesh elements often neces-sitates extensive model recalculations—for instance, inusing implicit FEM. Another negative factor is reductionin element size. Researchers have also reported defor-mation stability problems in the simulation of tissue cut-ting. This required a significant reduction of the timestep, thus rendering real-time simulation intractable.Finally, Molino, Bao, and Fedkiw report on work inwhich they decoupled the simulation and visualizationdomains.22 Their virtual node algorithm copies nodesand elements so that no new elements are created. Ele-ments are decomposed only in the visualization domain.A tetrahedron cannot be cut more than three times,however, and the surface resolution depends on the res-olution of the underlying tetrahedral mesh.

Topology adaptation approaches can amelioratesome of the problems.23,24 Their central idea is toapproximate a cutting path with existing vertices, edges,and polygons of the geometric model. This enables meshincisions without large increases in element count andalso without reductions in element size. Unfortunately,problems arise from degenerated elements, which canappear with unconditional node displacement in the

IEEE Computer Graphics and Applications 59

SuturingDissectionCutting Clamping

TranslocationGraspingPalpationCamera navigation

4 Typical simulations of MIS tasks: (a) basic and (b) advanced skills (courtesy ofSimbionix).

(a)

(b)

mesh. Also, the initial mesh resolution limits incisionapproximation quality. In addition, topology adaptationapproaches require an update of the undeformedmechanical model’s mesh parameters, which can be dif-ficult if the displacements are large. Steinemann et al.recently proposed a hybrid cutting approach for tetra-hedral meshes.25 It combines topological update via sub-division with adjustments of the existing topology. Inaddition, after the initial cut, local mesh regularizationimproves element quality. Moreover, the mechanicaland the visual models are decoupled, allowing differ-ent resolutions for the underlying mesh representations.This method can closely approximate an arbitrary, user-defined cut surface, while avoiding the creation of smallor badly shaped elements, thus preventing stabilityproblems in the subsequent deformation computation.

In addition to graphical rendering, cutting simulationresearch involves the development of a mechanistic modelof the cut for realistic force feedbackto the user. Most of these models arebased on experimental data collect-ed from tissue samples, Chantha-sopeephan, Desai, and Lau developedan instrumented hardware–softwaresystem for characterization of soft tis-sue’s mechanical response duringcutting.26 They performed ex vivocutting experiments with porcineliver using various cutting speeds andangles. They showed that althoughforce displacement behavior for dif-ferent combinations of cutting speedand angle has a characteristic pattern(loading, then a sudden puncture, then unloading), defor-mation resistance changes with the instrument’s speedand angle. Mahvash and Hayward developed a cuttingmodel based on a fracture mechanics approach.27 Theymodel cutting in three steps: deformation, tearing, andcutting. Their model assumes that energy is recoverableduring deformation, it is zero during tearing, and it is notrecoverable during fracture generation.

In tissue cutting during MIS, smoke and bleeding canoccur—for example, when a coagulation hook tearsapart the membrane tissue around organs. A coagula-tion simulation requires realistic smoke generation inreal time. Kuhnapfel, Cakmak, and Maab use animationtechniques to simulate smoke generation and fadingaway of the generated smoke after coagulation.10 Theyintegrate this approach in their MIS training environ-ment Kismet. De et al. simulate smoke generation usingthe PAFF method, which they developed for simulatingdeformable objects.20 They use the Lagrangian form ofthe Navier-Stokes formulation as the governing equa-tion to simulate smoke formation. Like smoke simula-tion, real-time simulation of bleeding is a recent surgicalsimulation research area. Basdogan, Ho, and Srinivasandeveloped a surface flow algorithm based on Navier-Stokes equations for bleeding simulation.23 They gen-erate an auxiliary mesh for the blood flow and project itto the incision area. Kuhnapfel, Cakmak, and Maabdeveloped a mass-spring model for simulation of arter-ial bleeding, irrigation, and suction.10 Zatonyi et al.

introduce a real-time approach based on computation-al fluid dynamics for simulating blood flow in the fluid-filled uterine cavity during hysteroscopy.28

Advanced surgical skills such as suturing, thread han-dling, and knot tying show similarities to cutting in manyways. Brown et al. implemented a suturing simulationenvironment for training in microsurgical skills.19 Theuser manipulates virtual blood vessels, sutures themtogether, and then ties the knot. MSM is the underlyingdeformation model for the vessels. Berkley et al. simulatesuturing and knot tying in real time on a 3D model of ahand.14 They use the banded-matrix approach, a fastfinite-element modeling technique developed for real-time deformation simulation, as the underlying deforma-tion model. During simulation, they visually display thehighly stressed areas on the hand model to prevent theuser from damaging the tissue during needle interactions.

System integrationA VR-based surgical simulator is

actually a human-computer inter-face consisting of a network of high-end hardware and softwarecomponents. Providing a realistictraining environment in whichtrainees act as if they are operatingon an actual patient is the simula-tor’s essential goal. Selection anddesign of simulator componentsdepend on the type of training thesimulator will serve.

A part-task trainer is a simulatorsystem that is designed to train a

particular surgical task. A part-task trainer’s hardwarecomponents typically include a computer with a 3Dgraphics accelerator for visualization of virtual organs,force feedback devices to simulate haptic sensations,and auditory interfaces to guide the trainee. Force-reflecting devices and actuators fitted with surgical toolhandles are embedded in a mannequin in a manner sim-ilar to standard MIS settings where surgical tools areinserted to the body through small incisions. Duringsimulations, the user manipulates actual surgical instru-ments attached to the force feedback devices in the man-nequin to interact with computer-generated anatomicalorgans. The computer monitor displays the organmanipulations (as the video monitor would do in MIS),and the haptic interfaces feed the reaction forces back tothe user. Using this set-up, a trainee can learn to executea specific procedure. The part-task trainer can monitorand record the trainee’s performance during the sessionfor further analysis.

A full-task (team) trainer is designed to train one ormore trainees at the same time on a full range of surgi-cal operations in a simulated OR. Compared with part-task trainers, full-task trainers require a larger space andenriched sensory feedback to simulate the OR environ-ment. Sensors and mechanical actuators (such asmechanical lungs, voice output, and drug delivery sys-tems) sensitive to the trainee’s actions are placed in themannequin and around the table to create a realistic ORenvironment for team training. We can envision a more

Feature Survey

60 March/April 2007

A VR-based surgical

simulator is actually a

human-computer interface

consisting of a network of

high-end hardware and

software components.

sophisticated team trainer in which multiple traineesequipped with stereo glasses, head trackers, andexoskeleton haptic devices enter an immersive roomsuch as CAVE, a projection-based VR system, which pro-vides a large-scale virtual environment. Visual imagesseamlessly projected on the walls would display a 3Danatomical model, information charts, and a floatingvirtual patient’s vital signs.

Integrating a part-task trainer’s software componentstypically requires the construction of hierarchical datastructures for storing objects’ geometric and materialproperties, a client-server model, and multithreadingand multiprocessing programming techniques to sepa-rate visual and haptic servo loops. Each sensory loophas its own requirements and demands a CPU timeaccordingly. Although a graphics update rate of 30 Hz issufficient for the human sensory system to perceive aflawless display of visual images, the human sensorysystem is far more sensitive to thehaptic update rate. For a realisticsense of touch and stable force inter-actions, the haptic update rateshould be as high as 1 kHz. More-over, displaying surface textures andvibrations requires even moredemanding rates: 5 to 10 kHz.

In a multithreading structure, aseparate thread can be assigned toeach sensory modality, and a priori-ty level for each thread can be set inadvance or dynamically during sim-ulations. Threads can share the same database, but theirproper synchronization in accessing the shared data-base is important for achieving real-time graphical- and haptic-rendering update rates. However, there is alwaysa trade-off between realism and real-time performance.For example, as tissue models get more sophisticated,update rates can easily drop below acceptable levels. Toachieve real-time update rates, adaptive subdivision tech-niques can progressively increase the model’s resolution.

To simulate procedures that require topological mod-ifications (such as cutting), we can decouple visual andhaptic models or implement hybrid approaches. Forexample, we can use more accurate but computational-ly more demanding deformation models such as FEMfor areas where topology is preserved. At the same time,to model cutting, we can use less accurate approachessuch as MSM, TMM, or PAFF, which allow easier topol-ogy changes.17,25 In addition to being efficient, the sim-ulator should provide realistic visual images. Instead ofstandard, static texture-mapping techniques, we can useview-dependent texture-mapping techniques to simu-late the complex glistening effect of the movements ofthe endoscopic-camera light.

Another issue in system integration is standardiza-tion of software architectures and languages that bindthe various simulator components efficiently. Althoughmany languages and formats exist for geometric repre-sentation of 3D objects, there are no standards for rep-resentation of physics-based deformable objects.Because there are different approaches to modeling thebehavior of deformable organs, and material properties

must be integrated uniformly in these models, the needfor a generic modeling architecture is obvious. Recent-ly, Chabanas and Promayon have presented the idea ofdeveloping a standard language called Physical ModelLanguage (PML), based on Extensive Markup Language(XML), for unified representation of continuous and dis-crete deformable models for surgical simulation.29 Cavu-soglu, Goktekin, and Tendick have developed theGeneral Interactive Physical Simulation Interface(GiPSi), an open-source and open-architecture softwaredevelopment framework for surgical simulation.30 GiPSiprovides a shared development environment and a stan-dard API to ensure modularity. With GiPSi, users cangenerate scenarios in which they can simulate differentorgans with different deformation models. A relatedendeavor is the Simulation Open Framework Architec-ture (SOFA) project, a concerted activity of severalgroups involved in surgical simulation. It targets an

extendible, open-source frameworkfor easy exchange of algorithmicblocks between research groups.

Currently, many research institu-tions are developing simulators, andseveral medical simulation compa-nies are offering integrated commer-cial systems. Leskovsky, Harders,and Szekely provide an overview ofexisting simulator systems devel-oped in academia.31 Table 1 (nextpage) lists current commercial MISpart-task simulators. Whereas some

companies offer a complete system consisting of cus-tomized software and hardware modules, others devel-op only software solutions, often in partnership withcompanies that provide the supplementary hardwareinterface. Basic-skills simulators aim at training in funda-mental skills such as navigation, hand-eye coordination,and basic tool-tissue interactions. Procedure simulators,on the other hand, provide an environment for trainingin more complex surgical skills. The training scenariosprovided by procedure systems vary from teaching com-plex tool-tissue interactions such as cutting and sutur-ing to teaching a complete surgical procedure such as alaparoscopic cholecystectomy.

Assessment, validation, and training transferMedical education focuses on knowledge-based and

skill-based training. In knowledge-based training,trainees become familiar with surgical instruments andtheir functionality. They learn to find anatomical land-marks, to differentiate healthy and pathological organsthrough visual cues such as color and texture, and totrack physiological changes such as heart rate and bloodpressure. Training sessions guide them through the stepsof surgical procedures such as cutting, suturing, andcoagulation. Skill-based training involves enhancementof the trainee’s visio-spatial, perceptual, and motor skillssuch as hand-eye coordination including depth percep-tion, navigation, aiming, and manipulation. Hand-eyecoordination is especially difficult in MIS because thelaparoscopic camera reflects 2D mirror images of handmovements and locations of anatomical landmarks.1

IEEE Computer Graphics and Applications 61

To achieve real-time update

rates, adaptive subdivision

techniques can

progressively increase

the model’s resolution.

For good coordination, surgeons must use cues such asthe sense of touch and the reflection of camera light fromorgans. Using the virtual counterparts of these cues insimulators, trainees can practice as much as necessaryto develop good hand-eye coordination. For example,

during a laparoscopic training session, trainees learn toaim the laparoscopic forceps at a target and move theforceps in the abdominal cavity to learn the allowablerange of applied movements and manipulate organs toexamine the allowable range of forces and torques

Feature Survey

62 March/April 2007

Table 1. Procedure and skill classification of commercial MIS part-task simulators. Blue: procedural simulator; red:basic-skills simulator; green: hardware interface with haptic feedback; purple: hardware interface without hapticfeedback.

Procedure Simulators with Simulators with custom hardware commercial hardware

General laparoendoscopic surgery 6, 8 1 (28-29), 12 (29), 19 (28), 21 (28), 24 (27) Laparoscopic cholecystectomy 9 1 (28-29), 20 (28), 22 (28), 25 (29-30) Laparoscopic colectomy 8Laparoscopic anastomosis 1 (28-29), 7 (27-28) Cardiovascular, endovascular intervention 2, 11, 14, 17Arthroscopy 13 (29) Gynecology, hysteroscopy 10, 15 23 (28) Bronchoscopy 16Upper, lower GI 3, 16Endourology 4 26 (31) Percutaneous access 5Endoscopic sinus surgery 18Laparoscopic gastric bypass, ventral hernia 1 (28-29) Basic MIS skills Simulator Camera navigation, exploration 1, 6, 8, 9, 10, 18, 19, 20, 21, 24, 25 Palpation 6, 19, 20, 24, 25Grasping 1, 9, 10, 19, 20, 21, 24, 25 Stretching 12, 19, 20, 25Translocation 12, 19, 20, 24Irrigation, suction 9, 10Advanced MIS skills Simulator Incision (cutting) 1, 9, 10, 12, 19, 20, 21, 25Dissection 1, 6, 8, 9, 10, 18, 19, 20, 22, 25, 26Coagulation 1, 6, 8, 9, 10, 12, 19, 20, 22, 25, 26Clamping (clip applying) 1, 8, 9, 10, 12, 19, 20, 21, 25Suturing 1, 6, 7, 8, 9, 10, 12, 21Thread manipulation, knot tying 1, 6, 7, 8, 9, 10, 12, 22Simulator key

1 Simbionix LAP Mentor 17 CATHI GmbH Cathi-Simulator

2 Simbionix ANGIO Mentor 18 Locheed Martin ESSS

3 Simbionix GI Mentor 19 Reaching RLT B

4 Simbionix URO Mentor 20 Reaching RLT BC

5 Simbionix PERC Mentor 21 Surgical Science LapSim Basic Skills

6 SimSurgery SEP 22 Surgical Science LapSim Dissection

7 SimSurgery VR Anastamosis Trainer, SimLap, SimCor 23 Surgical Science LapSim Gyn

8 Haptica ProMIS 24 Verefi EndoTower, RapidFire/SmartTutor, Head2Head

9 Select-IT VEST VSOne Cho 25 Xitact LapChol

10 Select-IT VEST VSOne Gyn 26 Melerit PelvicVision

11 Mentice Procedicus VIST 27 Immersion VLI

12 Mentice Procedicus MIST 28 Immersion LSW

13 Mentice Procedicus VA 29 Xitact ITP

14 Immersion Endovascular AccuTouch 30 Xitact IHP

15 Immersion Hysteroscopy AccuTouch 31 SensAble Technologies Phantom 1.5

16 Immersion Endoscopy AccuTouch

applied by the forceps. In addition, an expert surgeon’smovements in a procedure can be recorded in advanceand played back to the trainee through haptic devices.1

If the trainee moves out of the expert surgeon’s trajecto-ry, force feedback can return the trainee to that path.Additional guidance from visual cues and auditory feed-back strengthens the learning effect.

Concerns that simulators lack validity have adverse-ly affected the adoption of this technology in medicaltraining. Medical boards and councils’ growing interestin VR-based training has recently given rise to valida-tion studies.3,32 Validation is the verification of trainingeffectiveness. We can investigate a VR-based simulator’svalidity, or training effectiveness, at several levels.4

Face validity is the level of resemblance between thesimulated and real procedures. The factor contributingmost to face validity is the fidelity of the organ-forcemodels, as discussed earlier.

Content validity verifies thatmethods and metrics used for skillassessment are appropriate.

Construct validity examineswhether the assessment methods candifferentiate expert surgeons fromnovices. To compare novice andexpert performance, we must defineperformance metrics. Unlike tradi-tional medical training approaches,VR-based simulators can provideobjective measurement and assess-ment of technical competence. Inconventional methods, performancemeasurement and assessment de-pend on a supervisor’s qualitative, subjective evaluation.VR-based simulators, on the other hand, use quantitative,concrete metrics.3 During a training session, the systemcan record movements and applied forces and then eval-uate the trainee immediately using the performance met-rics. Quantitative performance measures include

■ task completion time,■ operational accuracy,■ hand motion economy,■ path length,■ work done by trainee (force times displacement),■ number of tasks completed successfully, ■ amount of unnecessary tissue damage, and■ excessive use of surgery material (for example,

clamps during clip applying).

Stylopoulos et al. developed a standard assessmentmethodology that uses several of these measures.33 Thismethodology merges the recorded values to quantifyoverall performance by a single number after the train-ing session. In addition, VR simulators can support pro-ficiency evaluation by generating learning curves basedon multiple training sessions.

Concurrent validity is the correlation between atrainee’s simulator performance and his or her OR per-formance. Finally, predictive validity is a prediction ofthe correlation between a trainee’s present simulatorperformance and his or her future OR performance. The

concurrent and predictive validations are related totraining transfer, also called VR-to-OR proof, which refersto the success of simulator training in actual perfor-mance, or how well simulator training transfers to thereal world.4

Future directionsMedical boards and accreditation councils in the US

and Europe have recognized the importance of VR-based training. The American Board of Medical Special-ties and the Accreditation Council on Graduate MedicalEducation identified the mastery of technical skillsunder the supervision of specialized instructors as amajor component of medical competence and approvedresidency training programs. They suggest that suchtraining take place outside the OR to support hands-onexperience with real patients in the OR. Similarly, theUK’s Royal College of Obstetricians and Gynaecologists,

in its 2002 report Discussion Docu-ment on Further Training for Doctorsin Difficulty, recommends the inclu-sion of VR trainers in surgery train-ing programs.

For more than 50 years, the avia-tion, aerospace, maritime, military,nuclear energy, and other high-riskindustries have been using simula-tors to train novices in difficult anddemanding tasks. The success offlight simulators is a convincingexample of the importance of sim-ulation technology for teachingskills that have an impact on human

lives. It also shows the long-term potential of simula-tion techniques. Just as flight simulators train pilots,VR-based systems can select, train, credential, andretrain physicians in the art and science of their profes-sion. Recent technology advances, reported inefficien-cy of traditional training approaches, and increasingsupport from medical professionals seem to direct next-generation medical training toward VR simulators.

A difference between MIS simulators and convention-al training approaches is that VR-based systems can pro-vide trainees with unusual training scenarios—an arterymistakenly severed during a procedure, perception dif-ficulty under limited visual or haptic cues, or device mal-function—to improve their problem-solving anddecision-making skills. Users who complete the train-ing program can regularly practice with the simulatorsto maintain their competency. In addition, surgeons canuse simulators as a preoperative planning environment.After entering patient-specific data into the simulator,the surgeon can plan and rehearse procedures beforeperforming the actual surgery. Finally, simulators canserve as a research and development environment inwhich expert surgeons develop, try, and test newsurgery techniques and devices.

Academia and industry have made significantprogress in surgical simulation, but many questions stillremain. Some are outside this article’s scope, but herewe highlight research directions that require furtherattention.

IEEE Computer Graphics and Applications 63

VR simulators can serve as

an R&D environment in

which expert surgeons

develop, try, and test new

surgery techniques

and devices.

Variations in anatomical models. A key aspect ofan effective surgical training simulator is its ability toprovide a wide range of anatomies and pathologies.Only by doing so can it present the trainee with a real-istic situation, because organs and pathologies in anytwo patients are never alike. So far, this aspect hasreceived little academic attention. Most projects use onlyone static organ model. They usually derive the model’sgeometry from an exemplary data set, acquired throughmedical-imaging technology from a patient or volun-teer. Researchers have paid even less attention to pro-viding problem cases—that is, pathological alterationsof the models. To that end, Sierra et al. recently pro-posed a method of automatic generation and addition ofvarious typical pathologies to healthy anatomies in hys-teroscopy simulation.34

Material properties of organs. We know little aboutthe material properties of organs intheir living state and native location.Moreover, there is great variation intissue properties of the same organas measured by different researchgroups. Although part of this varia-tion is due to differences in measure-ment devices, techniques, and sites,we know that tissue properties varywithin the same species dependingon age, weight, and gender. How toinclude these variations in organ-force models is an open question.This is an important issue because soft-tissue modelswith incorrect material properties can lead to negativetraining transfer.

Integrating material properties into deformablemodels. Another open question is how to set a defor-mation model’s parameters based on acquired in vivodata. Integrating the material properties of soft tissuesinto FEM models requires only a few parameters extract-ed from the experimental data. For example, Sedef,Samur, and Basdogan introduced a numerical methodfor real-time, linear, viscoelastic simulation of soft-tissuebehavior using experimental data.13 In contrast, mass-spring models need a considerable number of parame-ters, such as spring constants and mesh topology, whichmust be adapted to match real tissue’s behavior. Sever-al systems using mass-spring models rely on tediousmanual parameter tuning with the help of a medicalexpert. Bianchi et al. suggest an alternative approach:using genetic algorithms to optimize the model’s para-meters to approximate a known reference system.35

Physics-based tissue models. Another controversialissue is the fidelity, or realism, of organ-force models.The main question is how simple a simulation we can getaway with while preserving a level of fidelity betweenvirtual and real organ behavior that leads to positivetraining transfer. Thus, we must find out what is neededfor effective VR training before investing time and effortin developing complicated models of human organs andtool-tissue interactions. Another neglected issue is mod-

eling physiological organ responses. Many organs’ pri-mary function is to maintain a stable internal environ-ment despite external fluctuations. Hence, physiologicvariables such as blood pressure, heart rate, and bodytemperature change in response to surgical interven-tions, and should be included in organ-force models.

Graphical rendering and visual realism. The costof rendering polygonal objects grows drastically with thescene’s geometric complexity. Depending on this com-plexity, we can switch back and forth between low- andhigh-fidelity 3D geometric representations of organs for efficient graphical rendering (the level-of-detailapproach). An unexplored alternative solution is image-based rendering. In image-based rendering, we use a setof input images to create an intermediate data structurefrom which we can create new images of the scene later.Recent progress in this area suggests that new, efficient

image-based rendering algorithmsmight have many advantages overtraditional polygon rendering. Forexample, it is worthwhile to explorethe efficient implementation ofimage-based approaches in simulat-ing organ textures as an endoscopiccamera applies light to them duringan MIS. The reflected light generatescomplex visual effects and textures.Simulating these effects is challeng-ing and requires advanced lightinteraction models and texture-map-

ping methods. Another emerging area is point-based ren-dering. The lack of topology information in point setsmakes geometric modifications and adaptive refine-ments possible. For example, simulating tissue cuttingwith polygonal models requires polygon remeshing and subdivision, which can be eliminated with point-based approaches.

Human factors studies. There is great interest in val-idation of commercial surgical simulators. A missingcomponent of these investigations is the lack of detailedhuman factors studies. Even if we assume that surgicalsimulators’ hardware and software components willimprove one day to provide richer sensory stimulation,the trainee will still have to perceive the information.Hence, better understanding and measurement ofhuman perceptual and cognitive abilities is importantfor more effective training and training transfer. Estab-lishing visual, auditory, and haptic design guidelinescan aid the developers of VR-based surgical systems. Forexample, visual perception dominates human hapticperception, and if the visual display is not coherent withthe haptic display, the human sensory system can esti-mate the virtual object’s softness incorrectly. We canprogram a virtual organ to be perceived as softer sim-ply by altering the visual displacement of its nodesrather than its force response. Similarly, we can perhapsmodel an organ’s nonlinear force response as a series ofapproximated linear responses if the error in forceresponse is below the just noticeable difference ofhuman haptic perception.

Feature Survey

64 March/April 2007

Establishing visual,

auditory, and haptic design

guidelines can aid the

developers of VR-based

surgical systems.

Integrating VR-based simulators into the medicalcurriculum. Another critical issue is the integration ofa simulator system into the medical education program.Simulation system development should be needs dri-ven, not technology driven. First, the educators mustidentify the requirements of a trainer in the context ofthe education program. This usually requires a taskdecomposition of the surgical procedures to be taught.Then, they must identify the skills to be taught, whichcan be basic manipulative or higher-level cognitive skills.On the basis of this analysis, the educators can select theappropriate simulation setup. The open question of thelevel of realism needed to train in specific skills is relat-ed to this selection. Many surgeons believe that success-ful training must use highly realistic simulation trainers,even though low-fidelity training simulators have beeneffective in many domains.

Although technical efforts are going into surgical sim-ulator development, we still do not know all the require-ments of effective VR-based medical training. Is acomplex but accurate tissue model needed for effectivetraining? What is the role of force feedback in learninga particular procedure? How can we customize thetraining environment to the needs and skills of trainees?How much time should a trainee spend with a simula-tor? How well does the simulator training transfer to the real world? ■

AcknowledgmentsThe Scientific and Technological Research Council of

Turkey supported this work under contract MAG-104M283 and the student fellowship program BIDEB-2210.

References1. C. Basdogan et al., “Haptics in Minimally Invasive Surgi-

cal Simulation and Training,” IEEE Computer Graphics andApplications, vol. 24, no.2, 2004, pp. 56-64.

2. A. Liu et al., “A Survey of Surgical Simulation: Applications,Technology, and Education,” Presence, vol. 12, no. 6, 2003,pp. 599-614.

3. S. Srinivasan, D.P. Mital, and S. Haque, “A QuantitativeAnalysis of the Effectiveness of Laparoscopy andEndoscopy Virtual Reality Simulators,” Computers & Elec-trical Engineering, vol. 32, no. 4, 2006, pp. 283-298.

4. “Simulators for Training: Assessment, Validation andAcceptance Strategies,” Executive Summary of the Simu-lator for Training Workshop, Medicine Meets Virtual Reality Conf., 2003, www.medicalsim.org/virgil/Exec_Summary.pdf.

5. K.D. Reinig et al., “Real-Time Visually and Haptically Accu-rate Surgical Simulation,” Studies in Health Technology andInformatics, vol. 29, 1996, pp. 542-545.

6. R. Paget, M. Harders, and G. Szekely, “A Framework forCoherent Texturing in Surgical Simulators,” Proc. 13thPacific Conf. Computer Graphics and Applications, 2005, pp. 112-114; ftp://ftp.vision.ee.ethz.ch/publications/proceedings/eth_biwi_00354.pdf.

7. M. Kauer, Inverse Finite Element Characterization of SoftTissues with Aspiration Experiments, doctoral dissertation,

Inst. of Mechanical Systems, Swiss Fed. Inst. of Technolo-gy (ETH), Zurich, 2001.

8. M.P. Ottensmeyer, Minimally Invasive Instrument for In VivoMeasurement of Solid Organ Mechanical Impedance, doc-toral dissertation, Dept. of Mechanical Eng., Massachu-setts Inst. of Technology, 2001.

9. J. Kim, “Virtual Environments for Medical Training: Graph-ical and Haptic Simulation of Tool-Tissue Interactions,”doctoral dissertation, Dept. of Mechanical Eng., Massa-chusetts Inst. of Technology, 2003.

10. U. Kuhnapfel, H.K. Cakmak, and H. Maab, “EndoscopicSurgery Training Using Virtual Reality and DeformableTissue Simulation,” Computers & Graphics, vol. 24, no. 5,2000, pp. 671-682.

11. M. Bro-Nielsen and S. Cotin, “Real-Time VolumetricDeformable Models for Surgery Simulation Using FiniteElements and Condensation,” Computer Graphics Forum,vol. 15, no. 3, 1996, pp. 57-66.

12. C. Basdogan, C. Ho, and M.A. Srinivasan, “Virtual Envi-ronments for Medical Training: Graphical and Haptic Sim-ulation of Common Bile Duct Exploration,” IEEE/ASMETrans. Mechatronics, vol. 6, no. 3, 2001, pp. 267-285.

13. M. Sedef, E. Samur, and C. Basdogan, “Real-Time FiniteElement Simulation of Linear Viscoelastic Soft TissueBehavior Based on Experimental Data,” IEEE ComputerGraphics and Applications, vol. 6, no. 6, 2006, pp. 58-68.

14. J. Berkley et al., “Real-Time Finite Element Modeling forSurgery Simulation: An Application to Virtual Suturing,”IEEE Trans. Visualization and Computer Graphics, vol. 10,no. 3, 2004, pp. 314-325.

15. D. James and D.K. Pai, “A Unified Treatment of Elastosta-tic and Rigid Contact Simulation for Real Time Haptics,”Haptics-e, Electronic J. Haptics Research, vol. 2, no. 1, 2001,http://www.haptics-e.org/Vol_02/he-v2n1.pdf.

16. I.F. Costa and R. Balaniuk, “LEM—An Approach for RealTime Physically Based Soft Tissue Simulation,” Proc. IEEEInt’l Conf. Robotics and Automation (ICRA 01), IEEE Press,2001, vol. 3, pp. 2337-2343.

17. S. Cotin, H. Delingette, and N. Ayache, “A Hybrid ElasticModel Allowing Real-Time Cutting, Deformations andForce-Feedback for Surgery Training and Simulation,”Visual Computer, vol. 16, no. 8, 2000, pp. 437-452.

18. G. Picinbono, H. Delingette, and N. Ayache, “Non-linearand Anisotropic Elastic Soft Tissue Models for Medical Sim-ulation,” Proc. IEEE Int’l Conf. Robotics and Automation(ICRA 02), IEEE Press, 2002, vol. 2, pp. 1370-1375.

19. J. Brown et al., “A Microsurgery Simulation System,” Proc.4th Int’l Conf. Medical Image Computing and Computer-Assisted Intervention (Miccai 01), Springer, 2001, pp.137-144.

20. S. De et al., “Physically Realistic Virtual Surgery Using thePoint-Associated Finite Field (PAFF) Approach,” Presence:Teleoperators & Virtual Environments, vol. 15, no. 3, 2006,pp. 294-308.

21. D. Bielser et al., “A State Machine for Real-Time Cutting ofTetrahedral Meshes,” Graphical Models, vol. 66, no. 6,2004, pp. 398-417.

22. N. Molino, Z. Bao, and R. Fedkiw, “A Virtual Node Algo-rithm for Changing Mesh Topology during Simulation,”ACM Trans. Graphics, vol. 23, no. 3, 2004, pp. 385-392.

23. C. Basdogan, C. Ho, and M. Srinivasan, “Simulation of Tis-sue Cutting and Bleeding for Laparoscopic Surgery Using

IEEE Computer Graphics and Applications 65

Auxiliary Surfaces,” Medicine Meets Virtual Reality, J.D.Westwood et al., eds., IOS Press, 1999, pp. 38-44.

24. H.W. Nienhuys and A.F. van der Stappen, “A Surgery Simulation Supporting Cuts and Finite Element Defor-mation,” Proc. 4th Int’l Conf. Medical Image Computingand Computer-Assisted Intervention (Miccai 01), Springer,2001, pp. 153-160.

25. D. Steinemann et al., “Hybrid Cutting of DeformableSolids,” Proc. IEEE Conf. Virtual Reality (VR 06), IEEE CSPress, 2006, pp. 35-42.

26. T. Chanthasopeephan, J.P. Desai, and A.C.W. Lau, “Defor-mation Resistance in Soft Tissue Cutting: A ParametricStudy,” Proc. 12th Int’l Symp. Haptic Interfaces for VirtualEnvironment and Teleoperator Systems (Haptics 04), IEEECS Press, 2004, pp. 323-330.

27. M. Mahvash and V. Hayward, “Haptic Rendering of Cutting: A Fracture Mechanics Approach,” Haptics-e, Electronic J. Haptics Research, vol. 2, no. 1, 2001, http://www.haptics-e.org/Vol_02/he-v2n3.pdf.

28. J. Zatonyi et al., “Real-Time Synthesis of Bleeding for Vir-tual Hysteroscopy,” Medical Image Analysis, vol. 9, no. 3,2005, pp. 255-266.

29. M. Chabanas and E. Promayon, “Physical Model Language:Towards a Unified Representation for Continuous and Dis-crete Models,” Proc. Int’l Symp. Medical Simulation (ISMS04), LNCS 3078, Springer, 2004, pp. 256-266.

30. M.C. Cavusoglu, T. Goktekin, and F. Tendick. “GiPSi: AFramework for Open Source/Open Architecture SoftwareDevelopment for Organ Level Surgical Simulation,” IEEETrans. Information Technology in Biomedicine, vol. 10, no.2, 2006, pp. 312-321.

31. P. Leskovsky, M. Harders, and G. Szekely, “A Web-BasedRepository of Surgical Simulator Projects,” Proc. MedicineMeets Virtual Reality Conf. (MMVR 06), IOS Press, 2006,pp. 311-315.

32. F.J. Carter et al., “Consensus Guidelines for Validation ofVirtual Reality Surgical Simulators,” Surgical Endoscopy,vol. 19, no. 12, 2005, pp. 1523-1532.

33. N. Stylopoulos et al., “CELTS: A Clinically-Based Comput-er Enhanced Laparoscopic Training System,” Studies inHealth Technology and Informatics, vol. 94, 2003, pp. 336-342.

34. R. Sierra et al., “Generation of Variable Anatomical Mod-els for Surgical Training Simulators,” Medical Image Analy-sis, vol. 10, no. 2, 2006, pp. 275-285.

35. G. Bianchi et al., “Simultaneous Topology and StiffnessIdentification for Mass-Spring Models Based on FEM Ref-erence Deformations,” Proc. 7th Int’l Conf. Medical ImageComputing and Computer-Assisted Intervention (Miccai 04),Springer, 2004, pp. 293-301.

Cagatay Basdogan is a facultymember in the Mechanical Engineer-ing and Computational Sciences andEngineering departments of Koc Uni-versity, Istanbul, Turkey. His researchinterests include haptic interfaces,robotics, virtual environments, med-ical simulation, and biomechanical

modeling. Basdogan has a PhD in mechanical engineer-ing from Southern Methodist University. Contact him atcbasdogan@ku.edu.tr.

Mert Sedef is a graduate student inthe Department of Computational Sci-ences and Engineering at Koc Univer-sity, Instanbul, Turkey. His researchinterests include virtual environments,computer graphics, physically basedsimulation, haptic displays and ren-dering, and medical simulation and

robotics. Sedef has a BS in computer engineering from KocUniversity. Contact him at lsedef@ku.edu.tr.

Matthias Harders is a lecturerand senior researcher at the Comput-er Vision Lab of ETH (Swiss FederalInstitute of Technology) Zurich and aleader of the ETH Virtual Reality inMedicine Group. His research inter-ests include surgical simulation,human-computer interaction with

medical data, and haptic interfaces. Harders has a PhD ininformation technology and electrical engineering fromETH Zurich. Contact him at mharders@vision.ee.ethz.ch.

Stefan Wesarg is a senior re-searcher in the Cognitive Computingand Medical Imaging Department ofthe Fraunhofer Institute for Comput-er Graphics. His research interestsinclude medical imaging, visualiza-tion, and medical physics. Wesarg has a diploma in physics from the

German Cancer Research Center. Contact him at Stefan.Wesarg@igd.fhg.de.

Article submitted: 5 July 2006; revised: 14 Nov. 2006;

accepted: 28 Nov. 2006.

For further information on this or any other computingtopic, please visit our Digital Library at http://www.computer.org/publications/dlib.

Feature Survey

66 March/April 2007

on all conferences sponsored

by the IEEE Computer

Society

w w w . c o m p u t e r . o r g / j o i n

save25%

I E E E C o m p u t e r

S o c i e t y m e m b e r s