Titlebook: Bayesian and grAphical Models for Biomedical Imaging; First International M. Jorge Cardoso,Ivor Simpson,Annemie Ribbens Conference proceed

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Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differhape model to estimate the most likely relative position of two bone segments of an osteotomized bone. To investigate the added value of geometrical properties for planning, different geometrical features of the bone surface are being incorporated. The feasibility and accuracy of our proposed method

DEI

弯弯曲曲

An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokined by motion, which causes significant errors in tracer kinetic model analysis. Current intra-sequence registration methods for contrast enhanced data either assume restricted transformations (e.g. translation) or employ continuous optimization, which is prone to local optima. In this work, we propo

火光在摇曳

,Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilvely long and expensive. Considering what can be learned in the absence of such data, we estimate cohort-level biomarker trajectories by fitting cross-sectional data to a differential equation model, then integrating the fit. These fits inform our new stochastic differential equation model for synth

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Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can),s have mostly focused on technical issues with multiple comparisons and difficulties in interpreting .-values. While these critiques are valuable, we believe that they overlook the fundamental flaws of NHST in answering research questions. In this paper, we review major limitations inherent to NHST

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Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies,or each subject is provided as a normalized histogram of relevant features. In this paper, the resulting descriptors are used as phenotypes to identify genetic markers associated with the Chronic Obstructive Pulmonary Disease (COPD). Features extracted from images capture the heterogeneity of the di

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A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions,turn to isointensity. This resolution is thought to be due mostly to reabsorption of edema, but may also reflect other reparatory processes such as remyelination. Automatic identification of resolving portions of new lesions can provide a marker of repair, allow for automated analysis of MS lesion d

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Trigger-Point

Yuta Sudo,Toru Nakata,Toshikazu Katoy, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function. In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computationa