Katrine Jensen

A new possibility in thoracoscopic virtual reality simulation training: development and testing of a novel virtual reality simulator for video-assisted thoracoscopic surgery lobectomy

OBJECTIVES The aims of this study were to develop virtual reality simulation software for video-assisted thoracic surgery (VATS) lobectomy, to explore the opinions of thoracic surgeons concerning the VATS lobectomy simulator and to test the validity of the simulator metrics. METHODS Experienced VATS surgeons worked with computer specialists to develop a VATS lobectomy software for a virtual reality simulator. Thoracic surgeons with different degrees of experience in VATS were enrolled at the 22nd meeting of the European Society of Thoracic Surgeons (ESTS) held in Copenhagen in June 2014. The surgeons were divided according to the number of performed VATS lobectomies: novices (0 VATS lobectomies), intermediates (1-49 VATS lobectomies) and experienced (>50 VATS lobectomies). The participants all performed a lobectomy of a right upper lobe on the simulator and answered a questionnaire regarding content validity. Metrics were compared between the three groups. RESULTS We succeeded in developing the first version of a virtual reality VATS lobectomy simulator. A total of 103 thoracic surgeons completed the simulated lobectomy and were distributed as follows: novices n = 32, intermediates n = 45 and experienced n = 26. All groups rated the overall user realism of the VATS lobectomy scenario to a median of 5 on a scale 1-7, with 7 being the best score. The experienced surgeons found the graphics and movements realistic and rated the scenario high in terms of usefulness as a training tool for novice and intermediate experienced thoracic surgeons, but not very useful as a training tool for experienced surgeons. The metric scores were not statistically significant between groups. CONCLUSIONS This is the first study to describe a commercially available virtual reality simulator for a VATS lobectomy. More than 100 thoracic surgeons found the simulator realistic, and hence it showed good content validity. However, none of the built-in simulator metrics could significantly distinguish between novice, intermediate experienced and experienced surgeons, and further development of the simulator software is necessary to develop valid metrics.

Bayes Reconstruction of Missing Teeth

We propose a method for restoring the surface of tooth crowns in a 3D model of a human denture, so that the pose and anatomical features of the tooth will work well for chewing. This is achieved by including information about the position and anatomy of the other teeth in the mouth. Our system contains two major parts: A statistical model of a selection of tooth shapes and a reconstruction of missing data.We use a training set consisting of 3D scans of dental cast models obtained with a laser scanner, and we have build a model of the shape variability of the teeth, their neighbors, and their antagonists, using the eigenstructure of the covariance matrix, also known as Principle Component Analysis (PCA). PCA is equivalent to fitting a multivariate Gaussian distribution to the data and the principle directions constitute a linear model for stochastic data and is used both for a data reduction or equivalently noise elimination and for data analysis. However for small sets of high dimensional data, the log-likelihood estimator for the covariance matrix is often far from convergence, and therefore reliable models must be obtained by use of prior information. We propose a natural and intrinsic regularization of the log-likelihood estimate based on differential geometrical properties of teeth surfaces, and we show general conditions under which this may be considered a Bayes prior.Finally we use Bayes method to propose the reconstruction of missing data, for e.g. finding the most probable shape of a missing tooth based on the best match with our shape model on the known data, and we superior improved reconstructions of our full system.

Statistical modeling and removal of lipid membrane projections for cryo-EM structure determination of reconstituted membrane proteins

This paper describes steps in the single-particle cryo-EM 3D structure determination of membrane proteins in their membrane environment. Using images of the Kv1.2 potassium-channel complex reconstituted into lipid vesicles, we describe procedures for the merging of focal-pairs of exposures and the removal of the vesicle-membrane signal from the micrographs. These steps allow 3D reconstruction to be performed from the protein particle images. We construct a 2D statistical model of the vesicle structure based on higher-order singular value decomposition (HOSVD), by taking into account the structural symmetries of the vesicles in polar coordinates. Non-roundness in the vesicle structure is handled with a non-linear shape alignment to a reference, which ensures a compact model representation. The results show that the learned model is an accurate representation of the imaged vesicle structures. Precise removal of the strong membrane signals allows better alignment and classification of images of small membrane-protein particles, and allows higher-resolution 3D reconstruction.

Reconstructing teeth with bite information

We propose a method for restoring the surface of a tooth crown so that the pose and anatomical features of the tooth will work well for chewing. The system of teeth has been modeled with a 3D statistical multi-object shape model build from 3D scans of dental cast models. The restoration is carried out using the shape model statistics in a Bayesian framework to calculate the most probable tooth crown shape(s), given the fragments of one or more neighboring and opposing tooth crowns. The modeling of and reconstruction with the multi-object shape model has been realized by extending the model with a concept of elasticity that generalizes better to new teeth. The elasticity has been calculated from the surface curvature relations within and between each tooth sample, simulating a prior knowledge of the shape variation.

A novel assessment tool for evaluating competence in video-assisted thoracoscopic surgery lobectomy

BackgroundSpecific assessment tools can accelerate trainees’ learning through structured feedback and ensure that trainees attain the knowledge and skills required to practice as competent, independent surgeons (competency-based surgical education). The objective was to develop an assessment tool for video-assisted thoracoscopic surgery (VATS) lobectomy by achieving consensus within an international group of VATS experts.MethodThe Delphi method was used as a structured process for collecting and distilling knowledge from a group of internationally recognized VATS experts. Opinions were obtained in an iterative process involving answering repeated rounds of questionnaires. Responses to one round were summarized and integrated into the next round of questionnaires until consensus was reached.ResultsThirty-one VATS experts were included and four Delphi rounds were conducted. The response rate for each round were 68.9% (31/45), 100% (31/31), 96.8% (30/31), and 93.3% (28/30) for the final round where consensus was reached. The first Delphi round contained 44 items and the final VATS lobectomy Assessment Tool (VATSAT) comprised eight items with rating anchors: (1) localization of tumor and other pathological tissue, (2) dissection of the hilum and veins, (3) dissection of the arteries, (4) dissection of the bronchus, (5) dissection of lymph nodes, (6) retrieval of lobe in bag, (7) respect for tissue and structures, and (8) technical skills in general.ConclusionA novel and dedicated assessment tool for VATS lobectomy was developed based on VATS experts’ consensus. The VATSAT can support the learning of VATS lobectomy by providing structured feedback and help supervisors make the important decision of when trainees have acquired VATS lobectomy competencies for independent performance.

Removal of Vesicle Structures From Transmission Electron Microscope Images

In this paper, we address the problem of imaging membrane proteins for single-particle cryo-electron microscopy reconstruction of the isolated protein structure. More precisely, we propose a method for learning and removing the interfering vesicle signals from the micrograph, prior to reconstruction. In our approach, we estimate the subspace of the vesicle structures and project the micrographs onto the orthogonal complement of this subspace. We construct a 2D statistical model of the vesicle structure, based on higher order singular value decomposition (HOSVD), by considering the structural symmetries of the vesicles in the polar coordinate plane. We then propose to lift the HOSVD model to a novel hierarchical model by summarizing the multidimensional HOSVD coefficients by their principal components. Along with the model, a solid vesicle normalization scheme and model selection criterion are proposed to make a compact and general model. The results show that the vesicle structures are accurately separated from the background by the HOSVD model that is also able to adapt to the asymmetries of the vesicles. This is a promising result and suggests even wider applicability of the proposed approach in learning and removal of statistical structures.

Evaluating competency in video-assisted thoracoscopic surgery (VATS) lobectomy performance using a novel assessment tool and virtual reality simulation

BackgroundCompetency-based training has gained ground in surgical training and with it assessment tools to ensure that training objectives are met. Very few assessment tools are available for evaluating performance in thoracoscopic procedures. Video recordings would provide the possibility of blinded assessment and limited rater bias. This study aimed to provide validity evidence for a newly developed and dedicated tool for assessing competency in Video-Assisted Thoracoscopic Surgery (VATS) lobectomy.MethodsParticipants with varying experience with VATS lobectomy were included from different countries. Video recordings from participants’ performance of a VATS right upper lobe lobectomy on a virtual reality simulator were rated by three raters using a modified version of a newly developed VATS lobectomy assessment tool (the VATSAT) and analyzed in relation to the unitary framework (content, response process, internal structure, relation to other variables, and consequences of testing).ResultsFifty-three participants performed two consecutive simulated VATS lobectomies on the virtual reality simulator, leaving a total of 106 videos. Content established in previously published studies. Response process Standardized data collection was ensured by using an instructional element, uniform data collection, a special rating program, and automatic generation of the results to a database. Raters were carefully instructed in using the VATSAT, and tryout ratings were carried out. Internal structure Inter-rater reliability was calculated as intra-class correlation coefficients, to 0.91 for average measures (p < 0.001). Test/re-test reliability was calculated as Pearson’s r of 0.70 (p < 0.001). G-coefficient was calculated to be 0.79 with two procedures and three raters. By performing D-theory was found that either three procedures rated by two raters or five procedures rated by one rater were enough to reach an acceptable G-coefficient of ≥ 0.8. Relation to other variables Significant differences between groups were found (p < 0.001). The participants’ VATS lobectomy experience correlated significantly to their VATSAT score (p = 0.016). Consequences of testing The pass/fail score was found to be 14.9 points by the contrasting groups’ method, leaving five false positive (29%) and six false negatives (43%).ConclusionValidity evidence was provided for the VATSAT according to the unitary framework. The VATSAT provides supervisors and assessors with a procedure-specific assessment tool for evaluating VATS lobectomy performance and aids with the decision of when the trainee is ready for unsupervised performance.

On the bayesian reconstruction method for randomly oriented particles in cryo-EM

In this work, we address the problem of reconstructing the 3D structure of an object from a set of transmission electron microscopy (TEM) images, taken at unknown, random directions around the object. We use the expectation maximisation (EM) algorithm for finding the maximum a posteriori (MAP) estimates for the 3D structure from the marginal posterior, where the view orientations are integrated out. In comparison to previous work related to this single particle reconstruction application, we have made the following novel contributions. (1) We use Monte Carlo integration to approximate the expected complete data log posterior to reduce the computational complexity; (2) we use a uniform prior in the space of rotations instead of the space of rotation angles; (3) we use the positivity constraint for the reconstructed density that is both a physical constraint as well as it acts as a natural sparsity prior; (4) on the M-step we use a large scale, subspace trust-region method based on the interior-reflective Newton method for efficient computation of the reconstruction. We experimented the approach on cryo-electron microscopy (cryo-EM) protein images. The results are promising and show that the 3D structure can be robustly recovered with the proposed method in spite of the very low signal-to-noise ratio (SNR).

Bayesian epipolar geometry estimation from tomographic projections

In this paper, we first show that the affine epipolar geometry can be estimated by identifying the common 1D projection from a pair of tomographic parallel projection images and the 1D affine transform between the common 1D projections. To our knowledge, the link between the common 1D projections and the affine epipolar geometry has been unknown previously; and in contrast to the traditional methods of estimating the epipolar geometry, no point correspondences are required. Using these properties, we then propose a Bayesian method for estimating the affine epipolar geometry, where we apply a Gaussian model for the noise and non-informative priors for the nuisance parameters. We derive an analytic form for the marginal posterior distribution, where the nuisance parameters are integrated out. The marginal posterior is sampled by a hybrid Gibbs---Metropolis---Hastings sampler and the conditional mean and the covariance over the posterior are evaluated on the homogeneous manifold of affine fundamental matrices. We obtained promising results with synthetic 3D Shepp---Logan phantom as well as with real cryo-electron microscope projections.