Proceedings Article10.1109/ISBI45749.2020.9098592
Agglomerative Region-Based Analysis
Matt Higger,Demian Wassermann,Martha E. Shenton,Sylvain Bouix +3 more
- 03 Apr 2020
- pp 1702-1705
TL;DR: ARBA is an Agglomerative Clustering procedure, like Ward's method, which segments image sets in a common space to greedily maximize a likelihood function and is shown to increase sensitivity over VBA in a detection task on multivariate Diffusion MRI brain images.
read more
Abstract: A fundamental problem in brain imaging is the identification of volumes whose features distinguish two populations. One popular solution, Voxel-Based Analyses (VBA), glues together contiguous voxels with significant intra-voxel population differences. VBA's output regions may not be spatially consistent: each voxel may show a unique population effect. We introduce Agglomerative Region-Based Analysis (ARBA), which mitigates this issue to increase sensitivity. ARBA is an Agglomerative Clustering procedure, like Ward's method, which segments image sets in a common space to greedily maximize a likelihood function. The resulting regions are pared down to a set of disjoint regions that show statistically significant population differences via Permutation Testing. ARBA is shown to increase sensitivity over VBA in a detection task on multivariate Diffusion MRI brain images.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
References
Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
19.8K
New advances in the Clinica software platform for clinical neuroimaging studies
Alexandre Routier,Arnaud Marcoux,Mauricio Diaz Melo,Jérémy Guillon,Jorge Samper-González,Junhao Wen,Simona Bottani,Alexis Guyot,Elina Thibeau-Sutre,Marc Teichmann,Marie-Odile Habert,Stanley Durrleman,Ninon Burgos,Olivier Colliot +13 more
9.9K
The WU-Minn Human Connectome Project: An Overview
TL;DR: Progress made during the first half of the Human Connectome Project project in refining the methods for data acquisition and analysis provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
5.9K
Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference
TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).
5.2K
A reproducible evaluation of ANTs similarity metric performance in brain image registration.
TL;DR: This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups.
4.6K