Article
Operations Research & Management Science
Peng Zhang, Andreas Mang, Jiwen He, Robert Azencott, K. Carlos El-Tallawi, William A. Zoghbi
Summary: This operator splitting approach is developed to solve diffeomorphic matching problems for sequences of surfaces in three-dimensional space. The algorithms have been implemented in proprietary software at The Methodist Hospital for monitoring mitral valve strain through computer analysis of noninvasive patients echocardiographies.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Andrew H. Gee, Yufeng Zhao, Graham M. Treece, Manohar L. Bance
Summary: The study presents a fast, practicable and freely available method for estimating cochlear size and shape from clinical CT images. By fitting a template surface to the CT data, measurements of cochlear size, duct length, and a novel measure of basal turn non-planarity are obtained, which could potentially correlate with the risk of insertion trauma. The method yields promising results, with the locally affine deformation (LAD) method showing the best performance.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Biomedical
Kimerly A. Powell, Gregory J. Wiet, Brad Hittle, Grace Oswald, Jason P. Keith, Don Stredney, Steven Arild Wuyts Andersen
Summary: An atlas-based approach using 3D micro-slicing data and affine spatial registration was successful in segmenting cochlear microstructures in temporal bone images, for use in simulation software and potentially for pre-surgical planning and rehearsal.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2021)
Article
Medicine, General & Internal
Hugo Babel, Patrick Omoumi, Killian Cosendey, Hugues Cadas, Brigitte M. Jolles, Julien Favre
Summary: This study introduced a new method to standardize three-dimensional bone mineral density quantification in the knee using computed tomography and computational anatomy algorithms. The method showed excellent reliability and adequate reproducibility, making it suitable for research and clinical applications. Additionally, the method could potentially be adapted to quantify other bone parameters in three dimensions based on CT images or images acquired using different modalities.
JOURNAL OF CLINICAL MEDICINE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhipeng Ding, Marc Niethammer
Summary: Atlas building and image registration are important tasks in medical image analysis. This study explores the use of a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration with respect to the atlas, achieving better performance than other state-of-the-art image registration algorithms.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
Multidisciplinary Sciences
Cecilie Moller, Eduardo A. Garza-Villarreal, Niels Chr. Hansen, Andreas Hojlund, Klaus B. Baerentsen, M. Mallar Chakravarty, Peter Vuust
Summary: Our study shows that professional musicians have differences in brain morphology and function compared to non-musicians, with the grey and white matter structures reflecting their auditory specialization. Additionally, our findings suggest a correlation between reliance on visual cues in pitch discrimination and brain structures involved in audiovisual processing.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Nikos Makris, Richard Rushmore, Edward Yeterian
Summary: This study compares the structural connectivity of the superior fronto-occipital fascicle in monkeys and humans, and infers the connectivity patterns in the human brain through extrapolation from nonhuman primates. The information is represented using connectional matrices in a monkey-to-human translational system, providing valuable insights into the long association cortico-cortical fiber tracts in humans.
JOURNAL OF COMPARATIVE NEUROLOGY
(2023)
Article
Oncology
Lei Gao, Tahir I. Yusufaly, Casey W. Williamson, Loren K. Mell
Summary: This study developed an atlas-based method for fully automated segmentation of bony structures from whole-body CT and evaluated its performance compared to manual segmentation. The results showed that the segmentation method with a postprocessing module had higher accuracy and agreement compared to the method without postprocessing, with lower relative volume errors.
PRACTICAL RADIATION ONCOLOGY
(2023)
Article
Biochemical Research Methods
Bingyao Tan, Yin Ci Sim, Jacqueline Chua, Dheo Yusufi, Damon Wong, Ai Ping Yow, Calvin Chin, Anna C. S. Tan, Chelvin C. A. Sng, Rupesh Agrawal, Lekha Gopal, Ralene Sim, Gavin Tan, Ecosse Lamoureux, Leopold Schmetterer
Summary: Utilizing OCTA for visualization and characterization of microvascular abnormalities in ocular diseases deepens our understanding. Establishing a normative database can aid in accurately assessing individuals' perfusion status, while age also plays a role in retinal perfusion abnormalities.
BIOMEDICAL OPTICS EXPRESS
(2021)
Article
Neurosciences
Dongha Lee, Hae-Jeong Park
Summary: This study investigates the three-dimensional organization of neural fiber connections in the white matter of the brain. By analyzing connection distribution maps and related parameters, the study reveals the patterns of major fiber bundle connections in the white matter.
Article
Engineering, Chemical
Robert F. Phalen, Mark D. Hoover, Michael J. Oldham, Otmar Schmid, Laleh Golshahi
Summary: This paper discusses methods for defining respiratory tract anatomy required for inhaled aerosol deposition models. Advances in scanning living subjects or non-dissected excised lungs have improved our understanding of airway anatomy, yet challenges remain. While current knowledge is adequate for modeling medical and environmental exposure cases, there is still room for improvement in understanding respiratory tract anatomy and addressing remaining anatomical issues.
JOURNAL OF AEROSOL SCIENCE
(2021)
Article
Multidisciplinary Sciences
Peng Ji, Dan Chen, Lichao Wei
Summary: This study aimed to observe the condition of the corticospinal tract (CST) and its relationship with motor function in patients with acute cerebral infarction. Through magnetic resonance imaging and diffusion tensor imaging, the FA and DCavg values were analyzed and correlated with the modified Edinburgh Scandinavian scale score. The results showed that the FA and DCavg values in the cerebral infarction area were significantly lower than the healthy side, while the FA and DCavg values in the bilateral cerebral peduncles had no statistical significance. Conclusion: Diffusion tensor imaging can noninvasively display the degree of CST injury in cerebral infarction, helping to assess motor function impairment and predict prognosis.
JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Boulbaba Ben Amor, Sylvain Arguillere, Ling Shao
Summary: In this work, deep residual neural networks are used to solve the non-stationary ODE (flow equation) based on Euler's discretization scheme. The algorithm searches for an optimal partition of the space and computes optimal velocity vectors as affine transformations in each partition. The algorithm also predicts diffeomorphic transformations and is applied to 3D shape registration problems.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Review
Behavioral Sciences
Nestor Timonidis, Paul H. E. Tiesinga
Summary: Advanced techniques for large-scale cellular level data related to the mouse brain connectome have rapidly improved over the past decade. However, a detailed mapping of cell-type-specific projection patterns is still lacking. This work reviews neuroanatomical and data fusion techniques within a proposed Multimodal Connectomic Integration Framework to enhance the cellularly resolved mouse mesoconnectome.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2021)
Article
Computer Science, Theory & Methods
Nicolas Guigui, Xavier Pennec
Summary: In this work, we show that Taylor approximations of elementary constructions of Schild's ladder and the pole ladder can converge with quadratic speed. Moreover, we establish a new connection between Schild's ladder and the Fanning scheme and explain their convergence properties.
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Thomas Lartigue, Simona Bottani, Stephanie Baron, Olivier Colliot, Stanley Durrleman, Stephanie Allassonniere
Summary: In this paper, two families of Gaussian graphical model (GGM) inference methods, nodewise approach and penalised likelihood maximisation, are compared. The study demonstrates that when the sample size is small, both methods may result in graphs with either too few or too many edges compared to the real one. A composite procedure is proposed to address this issue, which explores a family of graphs with a nodewise numerical scheme and selects a candidate based on an overall likelihood criterion. This selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence compared to the other two methods, particularly when the number of observations is small.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Anupama Goparaju, Krithika Iyer, Alexandre Bone, Nan Hu, Heath B. Henninger, Andrew E. Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs, Nassir Marrouche, Shireen Y. Elhabian
Summary: Statistical shape modeling (SSM) is widely used in biology and medicine for quantitative analysis of anatomical shapes. Different SSM tools show varying levels of consistencies in capturing clinically relevant population-level variability. Validation frameworks and lesion screening methods are proposed for assessing shape models.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Mathematics, Applied
Yann Thanwerdas, Xavier Pennec
Summary: Several Riemannian metrics and families of Riemannian metrics are defined on the manifold of Symmetric Positive Definite (SPD) matrices. The principle of deformed metrics is used to relate the alpha-Pro crustes metrics to the mean kernel metrics. The principle of balanced bilinear forms is introduced to define the Mixed-Euclidean (ME) metrics, which generalize the Mixed-Power Euclidean (MPE) metrics and have links with (u, v)-divergences and (alpha, beta)-divergences of information geometry.
DIFFERENTIAL GEOMETRY AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Thomas Lartigue, Stanley Durrleman, Stephanie Allassonniere
Summary: This paper introduces a theoretical framework with state-of-the-art convergence guarantees for any deterministic approximation of the E step in the Expectation Maximisation algorithm. The authors analyze several approximations that fit into this framework and validate their effectiveness through theoretical and empirical results.
Article
Multidisciplinary Sciences
Igor Koval, Thomas Dighiero-Brecht, Allan J. Tobin, Sarah J. Tabrizi, Rachael Scahill, Sophie Tezenas du Montcel, Stanley Durrleman, Alexandra Durr
Summary: This study utilizes disease course mapping to forecast biomarker progression for individual carriers of the pathological CAG repeat expansions responsible for Huntington disease, in order to select participants at risk for progression and compute the power of trials for such an enriched population, ultimately reducing sample sizes and ensuring a more homogeneous group of participants.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics, Applied
Yann Thanwerdas, Xavier Pennec
Summary: This article investigates super-classes of kernel metrics beyond kernel metrics and studies which key results remain true. Additionally, an additional key result called cometric-stability is introduced, which is a crucial property to implement geodesics with a Hamiltonian formulation.
LINEAR ALGEBRA AND ITS APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Etienne Maheux, Igor Koval, Juliette Ortholand, Colin Birkenbihl, Damiano Archetti, Vincent Bouteloup, Stephane Epelbaum, Carole Dufouil, Martin Hofmann-Apitius, Stanley Durrleman
Summary: This study developed a statistical model, AD Course Map, for predicting the progression of Alzheimer's disease (AD) based on current medical and radiological data. The model was tested on a large dataset of over 96,000 cases and showed high accuracy in predicting clinical endpoints. By enriching the population with predicted progressors, the required sample size for trials could be reduced by 38% to 50%.
NATURE COMMUNICATIONS
(2023)
Article
Mathematics, Applied
Yann Thanwerdas, Xavier Pennec
Summary: This paper studies the geodesics of the Bures-Wasserstein distance on covariance matrices, including the properties of geodesics in each stratum and the minimizing geodesics joining two covariance matrices. Additionally, a review of the definitions related to geodesics is provided, which is helpful for the study of other spaces.
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS
(2023)
Article
Clinical Neurology
Cecile Di Folco, Raphael Couronne, Isabelle Arnulf, Graziella Mangone, Smaranda Leu-Semenescu, Pauline Dodet, Marie Vidailhet, Jean-Christophe Corvol, Stephane Lehericy, Stanley Durrleman
Summary: This study proposes a disease course map for Parkinson's disease (PD) and investigates the progression profiles of patients with or without rapid eye movement sleep behavioral disorders (RBD). The findings reveal distinct patterns of progression between PD patients with and without RBD, emphasizing the importance of understanding heterogeneity in PD progression for precision medicine.
MOVEMENT DISORDERS
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Benoit Sauty, Stanley Durrleman
Summary: Disease progression models are important for understanding degenerative diseases, but rarely used for entire medical images. This study combines a Variational Auto Encoder with a temporal linear mixed-effect model to learn a latent representation of the data and recover patterns of structural and metabolic alterations of the brain.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT I
(2022)
Proceedings Paper
Computer Science, Information Systems
K. Manouskova, V Abadie, M. Ounissi, G. Jimenez, L. Stimmer, B. Delatour, S. Durrleman, D. Racoceanu
Summary: Tau proteins play a role in Alzheimer's disease, and detecting and segmenting the aggregates is crucial. This study presents a 5-step pipeline that improves state-of-the-art performances in detecting and segmenting tau protein aggregates, providing valuable insights in the field.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Proceedings Paper
Engineering, Biomedical
Benoit Sauty, Stanley Durrleman
Summary: This study proposes a geometric framework for learning a manifold representation of longitudinal data to model disease progression of biomarkers. By learning the metric from the data, the method can fit longitudinal datasets well and provide a few interpretable parameters.
2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022)
(2022)
Article
Statistics & Probability
Clement Mantoux, Stanley Durrleman, Stephanie Allassonniere
Summary: This paper provides asymptotic convergence guarantees for a hierarchical statistical model for matrix data sets. The model captures the variability of matrices by modeling a truncation of their eigendecomposition and offers consistent MAP estimation.
ESAIM-PROBABILITY AND STATISTICS
(2022)
Article
Medical Informatics
Thomas Nedelec, Baptiste Couvy-Duchesne, Fleur Monnet, Timothy Daly, Manon Ansart, Laurene Gantzer, Beranger Lekens, Stephane Epelbaum, Carole Dufouil, Stanley Durrleman
Summary: This study analyzed health records from France and the UK and found significant associations between certain health conditions and the risk of developing Alzheimer's disease. These associations were particularly evident within a window of 2-10 years before the first diagnosis of Alzheimer's disease. These findings provide important insights for improving early prevention and intervention strategies for Alzheimer's disease.
LANCET DIGITAL HEALTH
(2022)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.