Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

Sargolzaei, Saman

doi:10.25148/etd.fi15032115

Cited by 1 publication

(1 citation statement)

References 106 publications

(145 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MRI continues to be a major modality in both clinical and pre-clinical settings, most notably due to its high spatial resolution, and varied contrast mechanisms for probing tissue microstructure and function. However, its use as an indirect readout of cellular activity and its relatively low temporal resolution compared to other modalities are potential limitations in the proper interpretation of MRI data[1], [2]. Anatomical imaging of brain using MRI has been cited amongst the most accurate techniques to detect structural abnormalities that arise in the diseased brain [3]–[9] .…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat

Sargolzaei

Cai

Wolahan

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

Accurate pre-clinical study reporting requires validated processing tools to increase data reproducibility within and between laboratories. Segmentation of rodent brain from non-brain tissue is an important first step in preclinical imaging pipelines for which well validated tools are still under development. The current study aims to clarify the best approach to automatic brain extraction for studies in the immature rat. Skull stripping modules from AFNI, PCNN-3D, and RATS software packages were assessed for their ability to accurately segment brain from non-brain by comparison to manual segmentation. Comparison was performed using Dice coefficient of similarity. Results showed that the RATS package outperformed the others by including a lower percentage of false positive, non-brain voxels in the brain mask. However, AFNI resulted in a lower percentage of false negative voxels. Although the automatic approaches for brain segmentation significantly facilitate the data stream process, the current study findings suggest that the task of rodent brain segmentation from T2 weighted MRI needs to be accompanied by a supervised quality control step when developmental brain imaging studies were targeted.

show abstract

Section: Introductionmentioning

confidence: 99%