GPU-friendly gallbladder modeling in laparoscopic cholecystectomy surgical training system

Zhang, J.; Zhou, Jinyun; Huang, Weimin; Qin, Jing; Yang, Tao; Liu, Jiang; Su, Yi; Chui, Chee-Kong; Chang, Stephen

doi:10.1016/j.compeleceng.2012.05.012

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…However, the only remaining challenge of the MSM is the selection of parameters (Er and Sun, 2001;Campos et al, 2013;Zhang et al, 2013). Many research projects have been developed to improve the underlying MSM parameter selections in terms of analytical and numerical approaches (Van Gelder, 1998;Maciel et al, 2003;Xu et al, 2010;Abulgasem et al, 2010), heuristic optimisation (Deussen et al, 1995;Bianchi et al, 2003;Pawlus et al, 2013), utilisation of Graphical Processing Unit (GPU) (Georgii et al, 2005;Tejada et al, 2005), and implementation of the Computer Unified Device Architecture (CUDA) approaches (Rasmusson et al, 2008;Leon et al, 2010;Zhang et al, 2010;Etheredge C.E, 2011;Campos et al, 2013;Zhang et al, 2013). Although the resultant models are able to simulate the deformable objects in real-time, there exists some limitations.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Rasmusson et al (2008) have proven that CUDA is a very interesting new platform to compute MSM in a surgical simulator. Ever since that, CUDA is becoming the limelight in the implementation of MSM in several approaches such as tissue cutting based on GPU and CUDA in surgical training, 3D tissue deformation (Zhang et al, 2010), CUDA-based FEM for elasticity simulation (Dick et al, 2011), laparoscopic surgical training system (Zhang et al, 2013) and so on (Etheredge, C.E., 2011;Rasmusson et al, 2008).…”

Section: Computer Unified Device Architecture (Cuda)mentioning

confidence: 99%

“…However, there is certain limitation such as overhead fast simulations (Zhang et al, 2010), parallelism techniques on GPU clusters (Dick et al, 2011) and realism of simulation due to the drawback of MSM (Zhang et al, 2013;Campus et al, 2013).…”

Section: Computer Unified Device Architecture (Cuda)mentioning

confidence: 99%

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Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

Tan¹,

Sidhu²

2019

Studies in Computational Intelligence

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Section: Research Backgroundmentioning

confidence: 99%

Section: Computer Unified Device Architecture (Cuda)mentioning

confidence: 99%

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Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

Tan¹,

Sidhu²

2019

Studies in Computational Intelligence

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“…The IRAS training system is developed to mimic the process of an experienced surgeon physically holding a trainee's hands to demonstrate movement of the laparoscopic instruments. IRAS consists of 3 major components: medical image processing and model reconstruction module, 10,11 surgical simulation platform, 12,13 and the robotic laparoscopic surgical trainer. 8,9 A simulated surgical procedure can be reproduced for training and demonstration.…”

Section: Instrument -Image-guided Robotic Assisted Surgical Simulator...mentioning

confidence: 99%

Can Robots Accelerate the Learning Curve for Surgical Training? An Analysis of Residents and Medical Students

Lau

Yang

Toe

et al. 2018

Ann Acad Med Singap

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Surgical traineeship has traditionally been based on a master apprentice model where learning takes place in the operating theatre. This approach has changed over the past few years with greater emphasis on surgical training taking place within the surgical skills laboratory. We developed a high fidelity simulator, the Image-guided Robotic Assisted Surgical simulator (IRAS) with an incorporated robotic guidance feature. The robot system is developed to mimic the process of an experienced surgeon physically holding a trainee's hands to demonstrate manoeuvring of the laparoscopic instruments. We aimed to assess the efficacy of incorporating robotic guidance into this high fidelity surgical simulator. Forty-two participants (13 surgical residents and 29 medical students) were recruited. Participants had one practice run for familiarisation and subsequently performed the virtual laparoscopic cholecystectomy (LC) once. Among the medical students, they were randomised to either a control or intervention group. They were tasked to perform a second- and third-timed LC assessment. Participants were asked to rate the simulator using a 5-point Likert scale questionnaire. IRAS rated favourably in hand-eye coordination and training bimanual dexterity (mean score: 4.1 and 4.0 among students, 3.4 and 3.4 among residents) though it faired suboptimally in realism. At baseline, residents were statistically faster compared to students (overall time: 418.9 vs 586.8 seconds, P = 0.001). Participants randomised to the intervention group consistently scored better. However, their overall time were not statistically significant from the control group. The robotic guidance capability of the IRAS is a key advantage of this simulator platform over the conventional platform. Key words: Cholecystectomy, Laparoscopy, Simulation training, Virtual reality

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“…For instance, cloth simulation [15,16], hair simulation [2], deformation of gallbladder [7] and soft tissue deformation [6]. Results from these research show efficiency in real-time interactive speed yet the accuracy and realism are doubted due to the selection of parameters [10,21,23].…”

Section: Previous Workmentioning

confidence: 99%

Fuzzy Estimation of Liver Stiffness in Modelling Liver Deformation

Yee

Dhillon

Sidhu

2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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The skill of a successful operation requires a thorough understanding on the particular organs, perhaps de facto surgery is complex nowadays. Therefore, surgical simulator has become an alternative demanding tool among the surgeons to practice and conducting pre-operation planning. Due to the ease of implementation of Mass Spring Model (MSM), the context of MSM has been extended to real-time invasive surgical simulator. However, the remaining drawback of MSM is the selection of parameter-stiffness. In this research, the fuzzy knowledge based system is introduced into the MSM. We present an improved MSM to simulate the liver deformation for surgery simulation. The underlying MSM is redesigned where the parameters are determined by using knowledge-based fuzzy logic. Comparison between different fuzzy approaches such as Interval Type-2 Fuzzy Sets (IT2), Mamdani and Sugeno are made. Among the three fuzzy approaches, IT2 has the highest similarity with the benchmark model. The stiffness values estimated by fuzzy approaches are in very good agreement with the benchmark result as each of the respective fuzzy approach graphs share the similar trend of displacement and velocity with the benchmark model.

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GPU-friendly gallbladder modeling in laparoscopic cholecystectomy surgical training system

Cited by 5 publications

References 10 publications

Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

Can Robots Accelerate the Learning Curve for Surgical Training? An Analysis of Residents and Medical Students

Fuzzy Estimation of Liver Stiffness in Modelling Liver Deformation

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