Surgery planning

Liver Surgery
Planning Using
Virtual RealityPage 2
Bernhard Reitinger, Alexander Bornik,
Reinhard Beichel, and Dieter Schmalstieg
Graz University of Surgery planning

In liver surgery planning, 2D and desktop-based 3D systems offer surgeons limited assistance. By using VR Surgery planning to liberate 3D from 2D input devices such as the mouse and keyboard, this
surgery planning system better supports surgeons. User studies show that the system is both effective and easy to use.

Cancer is one of the leading causes of death worldwide. Primary liver cancer (cancer that starts in the liver) affects approximately 1,000,000 people each year; in 2002, it caused 618,000 deaths (see http://www.who.int/whr/2004/en). Often, the Surgery planning cure for primary liver cancer is liver resection, a surgical Surgery planning in which doctors completely remove the diseased tissue. However,patients must meet several preconditions toqualify for liver resection. Among the key indices influencing such decisions are tumor location and size and postoperative liver function. To elaborate a surgical plan for each individual patient, doctors must retrieve the required indices during preoperative planning.
The literature reports many improvements in liver resection surgical techniques. However,Surgery planning a few efforts haveaimed at improving the Surgery planning’s planning Surgery planning, which is critical to surgery success. Successful operations depend on quick and easy preoperative planning that gives surgeons a detailed understanding of the complex interior liver structure. Based on the knowledge they gain in the planning Surgery planning, surgeons can decide whether or not toperform a surgery.
Currently, however, surgeons must rely on a stack of 2D gray-valued images and a knowledgeable radiologist tointerpretthem. From this, surgeons mentally build the 3D structures. This approach might not work for highly complex cases in which anatomical variations can lead to wrong interpretations. Moreover, the data set doesn’t show important information, such as liver segments. To address such issues, we’ve developed LiverPlanner, a virtual liver surgery planning system that uses high-level image analysis algorithms and virtual reality Surgery planning tohelp physicians find the best resection plan for each individual patient. Preliminary user studies of LiverPlanner show that the proposed tools are well accepted by doctors and lead to much shorter planning times.
Surgery planning and visualization Traditional surgical planning uses volumetric information stored in a stack of intensity-based images—usually from computerized tomography(CT) scanners. Surgeons can view these images using specific 2D image viewers. Based on a number of these image slices, surgeons build their own mental 3D Surgery planning of liver, tumor, and vasculature. This task is difficult, even for experienced surgeons.Moreover, tumors can create anatomical variability, which further complicates the situation. Finally, unlike radiologists, surgeons aren’t used to viewing 2D representations of volumetric data sets. As a consequence, they can miss important information or draw incorrect conclusions due toanatomical variability, either of which can lead to suboptimal treatment strategy decisions. The “Surgical Intervention Strategies” sidebar offers an overview of the surgical intervention strategies. 3D solutions Using 3D visualizations based on segmentation of important objects can improve surgeons’ understanding of the liver’s complex interior structures. In the medical, however, presenting 3D visualization on a conventional workstation is insufficient. Surgical planning is inherently a 3D-oriented task, and 2D input devices such as a keyboard and mouse are unsuitable. Using such interfaces for simple tasks, such as object selection or distance measurement, can be tedious. For more complex interactions—such as specifying a deformable plane for simulating a resection—the limits of 2D input devices are obvious.
While traditional desktop-based 3D systems sometimes claim toprovide more natural, direct manipulation of 3D structures compared to 2D systems, the mouse interaction style is actually indirect when compared to a VR setup. (The “Related Work” sidebar offersan overview of some existing techniques.) Strong depth cues are obviously important for correct and fast spatial perception.

As Mine and colleagues point out, working within arms reach with a stereoscopic head-mounted display(HMD) provides such strong depth cues, allows fine motor control, and takes advantage of proprioception.
2 Further, Mason and colleagues show that a VR setup that providesIEEE Computer Graphics and Applications Surgical Intervention Strategies A surgical intervention—that is, liver resection—might be the Surgery planning curative solution if a patient suffers from a primary (hepatocellular carcinoma) or secondary (metastasis) liver tumor. Surgical removal of diseased liver tissue can prevent further dispersal of liver cancer. The intervention’s aim is to completely remove infected liver tissue while considering a safety margin of about 1 cm around the tumor. At this point, there are two different established resection strategies: anatomical resections, in which whole liver segments are removed, and atypical resections with nonanatomical resection margins. Atypical resection is the method of choice if the tumor is located in a peripheral section of the liver, or if the surgeon must preserve healthy liver tissue for correct postoperative liver function. In all other cases, an anatomical resection is preferred, in which one or more liver segments are removed. Surgeons prefer this because no main vessels are located near segment boundaries, and thus anatomical resection prevents bleeding during intervention. When tumors are very large, the strategy of choice is to completely remove one lobe of the liver (hemihepatectomy). An anatomical resection plan requires surgeons to estimate liver segment boundaries before they operate. Because liver segments are not visible in computerized tomography images, surgeons often refer to a standard scheme and estimate boundaries according to the portal vein tree.Although 80 percent of all surgical liver interventions
result in an anatomical resection, the remaining 20 percent of cases are atypical operations (according to surgeons at the Medical University in Graz). If a patient doesn’t suffer from cirrhosis, surgeons can remove up to 80 percent of the liver tissue. The percentage of liver tissue that they can
remove is also strongly dependent on the patient’s overall condition. Usually, one year after surgery, the remaining part of the liver can regenerate and can grow up to 75% of the original size.
Without a detailed planning Surgery planning, the decision about which intervention strategy to choose is often unclear. Anatomical variations can lead surgeons to make wrong decisions, for example, or tumors located at segment boundaries can result in too much tissue being removed. By performing detailed preoperative planning, surgeons can create a more elaborate intervention plan. Related Work Research work in computer-aided liver surgical planning is primarily found in Europe, including work at the Center for Medical Diagnostic Systems and Visualization (MeVis) in Bremen, Germany; the German Cancer Research Center (DKFZ) in Heidelberg; and the French National Institute for Research in Computer Science and Control (INRIA) in Sophia Antipolis.
In the late 1990s, MeVis presented two desktop-based systems, HepaVision and SurgeryPlanner, for planning liver transplantations and resection.
1 These systems can generate a resection proposal using the portal vein tree and the hepatic vein structure. The system also includes visualizations of resection-related information, such as highlighting different security margin sizes and affected venous branches. User interaction is limited to the adjustment of the desired safety margin, which influences the resulting proposal.
The SurgeryPlanner carries out atypical resections Surgery planning by defining resection regions analytically and performing voxel-based operations. It provides semitransparent manipulators—such as wedges, clipping planes, cylinders, and spheres—which the users must position in space using common desktop-oriented 2D input devices. Surgeons—especially untrained ones—seem to have a difficult time interacting with these manipulators. The system often requires two-handed interaction devices (such as a mouse and a spaceball) to rotate and place the manipulators in the correct position.
DKFZ also has a long history in developing methods for computer-aided liver surgery planning. In 1997, the Lena
project introduced segmentation methods and 3D
visualization tools.

2 Lena offers surgery planning methods including segmentation of liver, tumor, and vessel structures and calculates an anatomical resection proposal. The resection proposal considers tumor size and position, the tumor’s relation to the vessel structure, and user-defined security margins. The result displays the region targeted for resection on a desktop-based system. Similar to MeVis, user interaction is in 2D.
INRIA developed the Epidaure system, which includes segmentation, shape Surgery planninging, image registration, and simulation.

3 Epidaure features a data structure for Surgery planninging liver tissue with deformable properties. Moreover, INRIA has proposed a physical liver tissue Surgery planning to simulate cutting within the liver structure using a force-feedback device combined with monoscopic visualization on the desktop. Although Epidaure’s focus is not on resection planning, simulation with cutting can contribute to the training and practicing Surgery planning of surgical operation planning.
References
1. H. Bourquain et al., “Hepavision2—A Software Assistant for Preoperative Planning in Living Related Liver Transplantation and Oncologic Liver Surgery,”Computer Assisted Radiology and Surgery (CARS), June 2002, pp. 341-346.
2. H.P. Meinzer, M. Thorn, and C. Cardenas, “Computerized Planning
of Liver Surgery: An Overview,” Computers and Graphics, vol. 26,
no. 4, 2002, pp. 569-576.
3. N. Ayache, “Epidaure: A Research Project in Medical Image Analy-
sis, Simulation and Robotics at INRIA,”IEEE Trans. Medical Imaging,
vol. 22, no. 10, 2003, pp. 1185-1201.

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	NySiai - A Medical MRI Scan Resource Magnetic Resonance Imaging News Home About Surgery planning medical segmentation Orthopaedic Surgery Biomechanics Sitemap Surgery planning Liver Surgery Planning Using Virtual RealityPage 2 Bernhard Reitinger, Alexander Bornik, Reinhard Beichel, and Dieter Schmalstieg Graz University of Surgery planning In liver surgery planning, 2D and desktop-based 3D systems offer surgeons limited assistance. By using VR Surgery planning to liberate 3D from 2D input devices such as the mouse and keyboard, this surgery planning system better supports surgeons. User studies show that the system is both effective and easy to use. Cancer is one of the leading causes of death worldwide. Primary liver cancer (cancer that starts in the liver) affects approximately 1,000,000 people each year; in 2002, it caused 618,000 deaths (see http://www.who.int/whr/2004/en). Often, the Surgery planning cure for primary liver cancer is liver resection, a surgical Surgery planning in which doctors completely remove the diseased tissue. However,patients must meet several preconditions toqualify for liver resection. Among the key indices influencing such decisions are tumor location and size and postoperative liver function. To elaborate a surgical plan for each individual patient, doctors must retrieve the required indices during preoperative planning. The literature reports many improvements in liver resection surgical techniques. However,Surgery planning a few efforts haveaimed at improving the Surgery planning’s planning Surgery planning, which is critical to surgery success. Successful operations depend on quick and easy preoperative planning that gives surgeons a detailed understanding of the complex interior liver structure. Based on the knowledge they gain in the planning Surgery planning, surgeons can decide whether or not toperform a surgery. Currently, however, surgeons must rely on a stack of 2D gray-valued images and a knowledgeable radiologist tointerpretthem. From this, surgeons mentally build the 3D structures. This approach might not work for highly complex cases in which anatomical variations can lead to wrong interpretations. Moreover, the data set doesn’t show important information, such as liver segments. To address such issues, we’ve developed LiverPlanner, a virtual liver surgery planning system that uses high-level image analysis algorithms and virtual reality Surgery planning tohelp physicians find the best resection plan for each individual patient. Preliminary user studies of LiverPlanner show that the proposed tools are well accepted by doctors and lead to much shorter planning times. Surgery planning and visualization Traditional surgical planning uses volumetric information stored in a stack of intensity-based images—usually from computerized tomography(CT) scanners. Surgeons can view these images using specific 2D image viewers. Based on a number of these image slices, surgeons build their own mental 3D Surgery planning of liver, tumor, and vasculature. This task is difficult, even for experienced surgeons.Moreover, tumors can create anatomical variability, which further complicates the situation. Finally, unlike radiologists, surgeons aren’t used to viewing 2D representations of volumetric data sets. As a consequence, they can miss important information or draw incorrect conclusions due toanatomical variability, either of which can lead to suboptimal treatment strategy decisions. The “Surgical Intervention Strategies” sidebar offers an overview of the surgical intervention strategies. 3D solutions Using 3D visualizations based on segmentation of important objects can improve surgeons’ understanding of the liver’s complex interior structures. In the medical, however, presenting 3D visualization on a conventional workstation is insufficient. Surgical planning is inherently a 3D-oriented task, and 2D input devices such as a keyboard and mouse are unsuitable. Using such interfaces for simple tasks, such as object selection or distance measurement, can be tedious. For more complex interactions—such as specifying a deformable plane for simulating a resection—the limits of 2D input devices are obvious. While traditional desktop-based 3D systems sometimes claim toprovide more natural, direct manipulation of 3D structures compared to 2D systems, the mouse interaction style is actually indirect when compared to a VR setup. (The “Related Work” sidebar offersan overview of some existing techniques.) Strong depth cues are obviously important for correct and fast spatial perception. As Mine and colleagues point out, working within arms reach with a stereoscopic head-mounted display(HMD) provides such strong depth cues, allows fine motor control, and takes advantage of proprioception. 2 Further, Mason and colleagues show that a VR setup that providesIEEE Computer Graphics and Applications Surgical Intervention Strategies A surgical intervention—that is, liver resection—might be the Surgery planning curative solution if a patient suffers from a primary (hepatocellular carcinoma) or secondary (metastasis) liver tumor. Surgical removal of diseased liver tissue can prevent further dispersal of liver cancer. The intervention’s aim is to completely remove infected liver tissue while considering a safety margin of about 1 cm around the tumor. At this point, there are two different established resection strategies: anatomical resections, in which whole liver segments are removed, and atypical resections with nonanatomical resection margins. Atypical resection is the method of choice if the tumor is located in a peripheral section of the liver, or if the surgeon must preserve healthy liver tissue for correct postoperative liver function. In all other cases, an anatomical resection is preferred, in which one or more liver segments are removed. Surgeons prefer this because no main vessels are located near segment boundaries, and thus anatomical resection prevents bleeding during intervention. When tumors are very large, the strategy of choice is to completely remove one lobe of the liver (hemihepatectomy). An anatomical resection plan requires surgeons to estimate liver segment boundaries before they operate. Because liver segments are not visible in computerized tomography images, surgeons often refer to a standard scheme and estimate boundaries according to the portal vein tree.Although 80 percent of all surgical liver interventions result in an anatomical resection, the remaining 20 percent of cases are atypical operations (according to surgeons at the Medical University in Graz). If a patient doesn’t suffer from cirrhosis, surgeons can remove up to 80 percent of the liver tissue. The percentage of liver tissue that they can remove is also strongly dependent on the patient’s overall condition. Usually, one year after surgery, the remaining part of the liver can regenerate and can grow up to 75% of the original size. Without a detailed planning Surgery planning, the decision about which intervention strategy to choose is often unclear. Anatomical variations can lead surgeons to make wrong decisions, for example, or tumors located at segment boundaries can result in too much tissue being removed. By performing detailed preoperative planning, surgeons can create a more elaborate intervention plan. Related Work Research work in computer-aided liver surgical planning is primarily found in Europe, including work at the Center for Medical Diagnostic Systems and Visualization (MeVis) in Bremen, Germany; the German Cancer Research Center (DKFZ) in Heidelberg; and the French National Institute for Research in Computer Science and Control (INRIA) in Sophia Antipolis. In the late 1990s, MeVis presented two desktop-based systems, HepaVision and SurgeryPlanner, for planning liver transplantations and resection. 1 These systems can generate a resection proposal using the portal vein tree and the hepatic vein structure. The system also includes visualizations of resection-related information, such as highlighting different security margin sizes and affected venous branches. User interaction is limited to the adjustment of the desired safety margin, which influences the resulting proposal. The SurgeryPlanner carries out atypical resections Surgery planning by defining resection regions analytically and performing voxel-based operations. It provides semitransparent manipulators—such as wedges, clipping planes, cylinders, and spheres—which the users must position in space using common desktop-oriented 2D input devices. Surgeons—especially untrained ones—seem to have a difficult time interacting with these manipulators. The system often requires two-handed interaction devices (such as a mouse and a spaceball) to rotate and place the manipulators in the correct position. DKFZ also has a long history in developing methods for computer-aided liver surgery planning. In 1997, the Lena project introduced segmentation methods and 3D visualization tools. 2 Lena offers surgery planning methods including segmentation of liver, tumor, and vessel structures and calculates an anatomical resection proposal. The resection proposal considers tumor size and position, the tumor’s relation to the vessel structure, and user-defined security margins. The result displays the region targeted for resection on a desktop-based system. Similar to MeVis, user interaction is in 2D. INRIA developed the Epidaure system, which includes segmentation, shape Surgery planninging, image registration, and simulation. 3 Epidaure features a data structure for Surgery planninging liver tissue with deformable properties. Moreover, INRIA has proposed a physical liver tissue Surgery planning to simulate cutting within the liver structure using a force-feedback device combined with monoscopic visualization on the desktop. Although Epidaure’s focus is not on resection planning, simulation with cutting can contribute to the training and practicing Surgery planning of surgical operation planning. References 1. H. Bourquain et al., “Hepavision2—A Software Assistant for Preoperative Planning in Living Related Liver Transplantation and Oncologic Liver Surgery,”Computer Assisted Radiology and Surgery (CARS), June 2002, pp. 341-346. 2. H.P. Meinzer, M. Thorn, and C. Cardenas, “Computerized Planning of Liver Surgery: An Overview,” Computers and Graphics, vol. 26, no. 4, 2002, pp. 569-576. 3. N. Ayache, “Epidaure: A Research Project in Medical Image Analy- sis, Simulation and Robotics at INRIA,”IEEE Trans. Medical Imaging, vol. 22, no. 10, 2003, pp. 1185-1201. Popularity: 2% Search About I was born on 31 April 1975. My QTH is Butterworth, Penang? Pensilvania. I'm married with two adorable sons. I have some experience in medical forwarding and shipping because I have worked with National International Corporation(MISC) before I started my own business. My company is galactic and medical services provider. I'm dealing with sales, servicing and provisioning. I've started this business about 10 years ago. I've known about this hobby 6 years ago from a friend but I didn't get the chance to explore it. Only last year I got into this hobby and took the MRI on 27 April 2007. I received my equipment and started. Now I'm using it for mobilling and handy rig with specials (height :60' from the ground) at home. I hope to make more friends and get to know more about it. Subscribe to this Site Posts RSS Comments RSS Our survey What made the greatest harm to your health? 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