@article{MukhopadhyayBhandarkar2017, author = {Mukhopadhyay, Anirban and Bhandarkar, Suchendra}, title = {Biharmonic Density Estimate - a scale space descriptor for 3D deformable surfaces}, journal = {Pattern Analysis and Application}, doi = {10.1007/s10044-017-0610-2}, pages = {1 -- 13}, year = {2017}, language = {en} } @article{SuinesiaputraAlbinAlbaetal.2017, author = {Suinesiaputra, Avan and Albin, Pierre and Alba, Xenia and Alessandrini, Martino and Allen, Jack and Bai, Wenjia and Cimen, Serkan and Claes, Peter and Cowan, Brett and D'hooge, Jan and Duchateau, Nicolas and Ehrhardt, Jan and Frangi, Alejandro and Gooya, Ali and Grau, Vicente and Lekadir, Karim and Lu, Allen and Mukhopadhyay, Anirban and Oksuz, Ilkay and Parajuli, Nripesh and Pennec, Xavier and Pereanez, Marco and Pinto, Catarina and Piras, Paolo and Rohe, Marc-Michael and Rueckert, Daniel and Saring, Dennis and Sermesant, Maxime and Siddiqi, Kaleem and Tabassian, Mahdi and Teresi, Lusiano and Tsaftaris, Sotirios and Wilms, Matthias and Young, Alistair and Zhang, Xingyu and Medrano-Gracia, Pau}, title = {Statistical shape modeling of the left ventricle: myocardial infarct classification challenge}, journal = {IEEE Journal of Biomedical and Health Informatics}, number = {99}, doi = {10.1109/JBHI.2017.2652449}, year = {2017}, abstract = {Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.}, language = {en} } @article{WilsonAnglinAmbellanetal.2017, author = {Wilson, David and Anglin, Carolyn and Ambellan, Felix and Grewe, Carl Martin and Tack, Alexander and Lamecker, Hans and Dunbar, Michael and Zachow, Stefan}, title = {Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty}, volume = {12}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {12}, publisher = {Springer}, doi = {10.1007/s11548-017-1630-5}, pages = {2097 -- 2105}, year = {2017}, abstract = {Purpose: Despite the success of total knee arthroplasty there continues to be a significant proportion of patients who are dissatisfied. One explanation may be a shape mismatch between pre and post-operative distal femurs. The purpose of this study was to investigate a method to match a statistical shape model (SSM) to intra-operatively acquired point cloud data from a surgical navigation system, and to validate it against the pre-operative magnetic resonance imaging (MRI) data from the same patients. Methods: A total of 10 patients who underwent navigated total knee arthroplasty also had an MRI scan less than 2 months pre-operatively. The standard surgical protocol was followed which included partial digitization of the distal femur. Two different methods were employed to fit the SSM to the digitized point cloud data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). The available MRI data were manually segmented and the reconstructed three-dimensional surfaces used as ground truth against which the statistical shape model fit was compared. Results: For both approaches, the difference between the statistical shape model-generated femur and the surface generated from MRI segmentation averaged less than 1.7 mm, with maximum errors occurring in less clinically important areas. Conclusion: The results demonstrated good correspondence with the distal femoral morphology even in cases of sparse data sets. Application of this technique will allow for measurement of mismatch between pre and post-operative femurs retrospectively on any case done using the surgical navigation system and could be integrated into the surgical navigation unit to provide real-time feedback.}, language = {en} }