Virtual reality training and assessment in laparoscopic rectum surgery

Background: Virtual-reality (VR) based simulation techniques offer an efficient and low cost alternative to conventional surgery training. This article describes a VR training and assessment system in laparoscopic rectum surgery. Methods: To give a realistic visual performance of interaction between membrane tissue and surgery tools, a generalized cylinder based collision detection and a multi-layer mass-spring model are presented. A dynamic assessment model is also designed for hierarchy training evaluation. Results: With this simulator, trainees can operate on the virtual rectum with both visual and haptic sensation feedback simultaneously. The system also offers surgeons instructions in real time when improper manipulation happens. The simulator has been tested and evaluated by ten subjects. Conclusions: This prototype system has been verified by colorectal surgeons through a pilot study. They believe the visual performance and the tactile feedback are realistic. It exhibits the potential to effectively improve the surgical skills of trainee surgeons and significantly shorten their learning curve. © 2014 John Wiley & Sons, Ltd

Modelling Rod-like Flexible Biological Tissues for Medical Training

This paper outlines a framework for the modelling of slender rod-like biological tissue structures in both global and local scales. Volumetric discretization of a rod-like structure is expensive in computation and therefore is not ideal for applications where real-time performance is essential. In our approach, the Cosserat rod model is introduced to capture the global shape changes, which models the structure as a one-dimensional entity, while the local deformation is handled separately. In this way a good balance in accuracy and efficiency is achieved. These advantages make our method appropriate for the modelling of soft tissues for medical training applications

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Essential techniques for laparoscopic surgery simulation

Laparoscopic surgery is a complex minimum invasive operation that requires long learning curve for the new trainees to have adequate experience to become a qualified surgeon. With the development of virtual reality technology, virtual reality-based surgery simulation is playing an increasingly important role in the surgery training. The simulation of laparoscopic surgery is challenging because it involves large non-linear soft tissue deformation, frequent surgical tool interaction and complex anatomical environment. Current researches mostly focus on very specific topics (such as deformation and collision detection) rather than a consistent and efficient framework. The direct use of the existing methods cannot achieve high visual/haptic quality and a satisfactory refreshing rate at the same time, especially for complex surgery simulation. In this paper, we proposed a set of tailored key technologies for laparoscopic surgery simulation, ranging from the simulation of soft tissues with different properties, to the interactions between surgical tools and soft tissues to the rendering of complex anatomical environment. Compared with the current methods, our tailored algorithms aimed at improving the performance from accuracy, stability and efficiency perspectives. We also abstract and design a set of intuitive parameters that can provide developers with high flexibility to develop their own simulators

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Development of A Kinetic Model For Loop-Free Colonoscopy Technology

The colonoscope is an important tool in diagnosis and management of diseases of the colon. One of the ongoing challenges with this device is that the colonoscope may form a loop together with the colon during the procedure. The result of the loop is that further insertion of the scope in the colon may not be possible. The loop may also cause risks of perforation of the colon and pain in the patient. There are currently several existing devices to overcome loop formation in colonoscopy, some of which have been introduced in clinical work. However, empirical assessment shows that these devices do not work very well. This is the motivation for the research presented in this thesis. In this thesis, a new paradigm of thinking, “doctor-assisted colonoscopy,” is proposed to overcome loop formation. In this new approach, the physician’s role is enhanced with new information that is acquired by sensors outside the human body and inferred from the mathematical model. It is referred to as a kinetic model due to the fact that this model describes the kinetic behaviour of the scope. This thesis is devoted to development of this kinetic model. In this study, the model of the colonoscope and the model of the colon are developed based on the Timoshenko beam theory, and parameters in both models are determined by the experiments. The following conclusions then are made: (1) self-locking of the colonoscope is the most basic cause for a loop to occur, while structural instability of the colonsocope is dependent on the self-locking; (2) both the scope and the colon can be well represented with the Timoshenko beam elements and the Linear Complementary Problem (LCP) formulation derived from Signorini’s law, and Coulom’s law for representation of interactions between the colon and scope is adequate; (3) there are effects from the location, looping, and tip deflection of the scope on flexural rigidity of the scope. Approximately, the flexural rigidity of the CF-Q160L colonoscope ranges from 300 to 650 N•cm2, and its accuracy is proven by a good agreement between the model predicted result and experimental result; (4) Rayleigh damping for the CF-Q160L colonoscope depends more on the mass matrix [M] of the colonoscope than the stiffness matrix [K], which is evident by the large coefficient value of “alpha” (0.3864) and the small coefficient value of “beta” (0.0164). The contributions of this thesis are: (1) the finding that the main cause of the loop is not structural instability of the colonoscope but rather self-locking of the colonoscope, which could lead to design of a “new-generation” colonoscope to avoid the loop; (2) a systematic evaluation of the existing colonoscopy technologies based on the well-proven Axiomatic Design Theory (ADT), which will serve as a guideline for the development of future new colonoscopes in future; (3) an approach to developing a kinetic model of the colonoscope useful to modeling similar objects such as a catheter guide-wire; (4) a novel ex-vivo colonoscopy test-bed with the kinetic and kinematic measurements useful for validation of new designs in colonoscopy technology and also useful for training physicians who perform the colonoscopy procedure; and (5) a new paradigm of thinking for colonoscopy called “doctor-assisted colonoscopy,” which has potential applications to other medical procedures such as catheter-based procedures

Consistency check of planned adaptive option on helical tomotherapy.

This study aims to evaluate a new Planned Adaptive software (TomoTherapy Inc., Madison, WI) of the helical tomotherapy system by retrospective verification and adaptive re-planning of radiation treatment. Four patients with different disease sites (brain, nasal cavity, lungs, prostate) were planned in duplicate using the diagnostic planning kVCT data set and MVCT studies of the first treatment fraction with the same optimization parameters for both plan types. The dosimetric characteristics of minimum, maximum, and mean dose to the targets as well as to organs at risk were compared. Both sets of plans were used for calculation of dose distributions in a water-equivalent phantom. Corresponding measurements of these plans in phantom were carried out with the use of radiographic film and ion chamber. In the case of the lung and prostate cancer patients, changes in dosimetric parameters compared to data generated with the kVCT study alone were less than 2%. Certain changes for the nasal cavity and brain cancer patients were greater than 2%, but they were explained in part by anatomy changes that occurred during the time between kVCT and MVCT studies. The Planned Adaptive software allows for adaptive radiotherapy planning using the MVCT studies obtained by the helical tomotherapy imaging system