scispace - formally typeset
M

Mert R. Sabuncu

Researcher at Cornell University

Publications -  129
Citations -  10846

Mert R. Sabuncu is an academic researcher from Cornell University. The author has contributed to research in topics: Image registration & Image segmentation. The author has an hindex of 44, co-authored 129 publications receiving 9291 citations. Previous affiliations of Mert R. Sabuncu include Siemens & Princeton University.

Papers
More filters
Journal ArticleDOI

The influence of head motion on intrinsic functional connectivity MRI.

TL;DR: Head motion was associated with decreased functional coupling in the default and frontoparietal control networks--two networks characterized by coupling among distributed regions of association cortex and other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions.
Journal ArticleDOI

Individual Variability in Functional Connectivity Architecture of the Human Brain

TL;DR: Using repeated-measurement resting-state functional MRI to explore intersubject variability in connectivity revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability.
Journal ArticleDOI

Multi-Atlas Segmentation of Biomedical Images: A Survey

TL;DR: Multi-atlas segmentation (MAS) is becoming one of the most widely used and successful image segmentation techniques in biomedical applications as mentioned in this paper, and it has been widely used in medical image classification.
Journal ArticleDOI

A Generative Model for Image Segmentation Based on Label Fusion

TL;DR: This manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach, and indicates that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms.
Journal ArticleDOI

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

TL;DR: The theory behind LME models is presented, it is contrasted with other popular approaches in the context of LNI, and the results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.