This quantity can be studied in terms of mutual information between disease status and Received: 27 April 2017 anatomical form. Specialty section: The trend toward a quantitative, task based, understanding of medical images leads to the simple This article was submitted to goal of answering “how many bits of information would one expect a medical image to contain Brain Imaging Methods, about disease status?” Knowing the answer to this question could impact a clinician’s decision a section of the journal of whether or not to order an imaging study, particularly in the case where it involves ionizing Frontiers in Neuroscience radiation. edu/wp-content/uploads/ how_to_apply/ ADNI_Acknowledgement_List.pdf 1. A Keywords: computational anatomy, diffeomorphometry, neuroimaging, anatomical prior, entropy, complexity, rate complete listing of ADNI investigators distortion can be found at. contributed to the design and This work represents a first step towards quantifying the amount of information ordering implementation of ADNI and/or provided data but did not participate a neuroimaging study can provide about disease status. The key portion is “the investigators within the ADNI than 35 bits, and at 1.5 mm error all structures can be described with less than 12 bits. Initiative (ADNI) database We find that the at 1 mm accuracy all subcortical structures can be described with less (). We study the shape of subcortical gray matter structures in the human brain †Data used in preparation of this article were obtained from the through diffeomorphic transformations that relate them to a template, using data from the Alzheimer’s Disease Neuroimaging Alzheimer’s Disease Neuroimaging Initiative to train a multivariate Gaussian prior model.
#HIPPOCAMPUS ANATOMY NEUROANATOMY CODE#
Tward code rate (number of bits), and its relationship geometric accuracy at clinically relevant scales. Mallar Chakravarty, McGill University, Canada In this work we devise a strategy for discrete coding of anatomical form as described *Correspondence: by a Bayesian prior model, quantifying the entropy of this representation as a function of Daniel J. Miller for the Alzheimer’s Disease Neuroimaging Initiative † Reviewed by: Fabio Grizzi, Center for Imaging Science, Department of Biomedical Engineering, Kavli Neuroscience Discovery Institute, Johns Hopkins Humanitas Clinical and Research University, Baltimore, MD, United States Center, Italy M. On the Complexity of Human Neuroanatomy at the Millimeter Morphome Scale: Developing CodesĮdited by: and Characterizing Entropy Indexed Pedro Antonio Valdes-Sosa, Joint China-Cuba Laboratory for to Spatial Scale Frontier Research in Translational Neurotechnology, China Daniel J. On the Complexity of Human Neuroanatomy at the Millimeter Morphome Scale: Developing Codes and Characterizing Entropy Indexed To
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