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Trends in Biotechnology
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2020
Computational anatomy (CA) is a discipline within medical imaging focusing on the study of anatomical shape and form at the visible or gross anatomical scale of morphology. It comprises the development and application of computational, mathematical data for modeling and simulation of biological structures and organisms. Its emphasis is on the anatomical structures being imaged, rather than the medical imaging devices. It is like computational linguistics, a discipline that focuses on the linguistic structures rather than the sensor acting as the transmission and communication medium. With the advent of dense three-dimensional measurements through technologies such as MRI, CA has emerged as a subfield of medical imaging and bioengineering for mining anatomical coordinate systems. In CA, the diffeomorphism group is used to study different coordinate systems via coordinate transformations as generated via the Lagrangian and Eulerian velocities of flow from one anatomical configuration to another. CA intersects the study of Riemannian manifolds where groups of diffeomorphisms are the central focus, intersecting with emerging high-dimensional theories of shape emerging from the field of shape statistics. The metric structures in CA are related in spirit to morphometrics, with the distinction that CA focuses on an infinite-dimensional space of coordinate systems transformed by a diffeomorphism, hence the central use of the terminology diffeomorphometry, the metric space study of coordinate systems via diffeomorphisms. At CA’s heart is the comparison of shape by recognizing in one shape the other. This connects it to D’Arcy Wentworth Thompson’s developments in On Growth and Form which has led to scientific explanations of morphogenesis, the process by which patterns are formed in biology.
The New Zealand Genetic Frontotemporal Dementia Study (FTDGeNZ): a longitudinal study of pre-symptomatic biomarkers
Published in Journal of the Royal Society of New Zealand, 2023
Brigid Ryan, Ashleigh O’Mara Baker, Christina Ilse, Kiri L. Brickell, Hannah M. Kersten, Joanna M. Williams, Donna Rose Addis, Lynette J. Tippett, Maurice A. Curtis
Cortical reconstruction and volumetric segmentation of T1 data will be performed with the Computational Anatomy Toolbox (cat12; Gaser et al. 2022; implemented in SPM12), and measures of volumetrics, cortical thinning and grey matter intensity will be compared between carriers and non-carriers in a priori ROIs. Lesion probability maps of white matter hyperintensities generated from the T2 Flair image using the lesion prediction algorithm (Schmidt et al. 2012; implemented in the LST toolbox, www.statistical-modelling.de/lst.html), and regional cerebral blood flow maps generated from pcASL images using BASIL – Bayesian Inference for Arterial Spin Labelling MRI (Chappell et al. 2009; implemented in FSL, https://fsl.fmrib.ox.ac.uk/) will be compared between carriers and non-carriers.
Speech Map: a statistical multimodal atlas of 4D tongue motion during speech from tagged and cine MR images
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2019
Jonghye Woo, Fangxu Xing, Maureen Stone, Jordan Green, Timothy G. Reese, Thomas J. Brady, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri
Since its inception of ‘computational vocal tract anatomy’ (Woo et al. 2015), efforts have been made to understand and model not just the anatomy itself but the anatomical changes of the tongue over time and its variations across a population (Stone et al. 2016; Woo et al. 2016). Although this first effort only accounts for the tongue, we can expand the reference anatomic configuration and the motion fields to include the whole vocal tract. In addition, although registration of each subject with the atlas has the effect of warping the vocal tract that would not be expected to preserve articulatory-acoustic relations, the diffeomorphisms used to warp each individual subject to the atlas encode information on parameters that reveal characteristics of each individual’s vocal tract such as vocal tract area function. This is similar to the approaches used in computational anatomy (Miller 2004).
Detection of Alzheimer’s disease from temporal lobe grey matter slices using 3D CNN
Published in The Imaging Science Journal, 2022
R. Divya, R. Shantha Selva Kumari
Structural T1 MRI images obtained at 3T acquired from different scanners, such as GE, Philips and Siemens at different sites, are included. MATLAB 2020b is used to check whether the origin points to the anterior commissure using statistical parametric mapping (SPM12) toolbox (fil.ion.ucl.ac.uk/spm/); otherwise, it is corrected manually. Computational anatomy toolbox (CAT12) (dbm.neuro.uni-jena.de/cat/) is used to perform skull stripping by the growth of adaptive probability region and normalization to the Montreal Neurological Institute (MNI) template. All the volumes are bias-field corrected and the normalized skull-stripped brain volumes are segmented. The preprocessing step is shown in Figure 2.