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Hippocampal shrinkage is a main biomarker for the detection of Alzheimer's disease and Temporal lobe Epilepsy (TLE). Mostly, developing methods for the hippocampus segmentation are unable to initialize automatically due to its low contrast boundary and uncertain position with respect to the wide range of human brain size. This paper will describe how to reduce the search area in brain MRI to determine...
Automatic segmentation of cortical and subcortical structures is commonplace in brain MRI literature and is frequently used as the first step towards quantitative analysis of structural and functional neuroimaging. Most approaches to brain structure segmentation are based on propagation of anatomical information from example MRI datasets, called atlases or templates, that are manually labeled by experts...
In automated hippocampus segmentation, issues related to ground truth rater variability, subject variability and variability of software segmentation accuracy are investigated in the context of 3 publicly available, out-of-the-box software packages. Ground truth variability among three manual raters is controlled using a majority voting based label fusion scheme and observed subject variability underpins...
Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical...
Segmentation of hippocampus (Hc) from the human brain is a significant task in the medical field for the identification of abnormalities in the brain functions. In this paper, we propose a method to segment the hippocampus from Magnetic Resonance Imaging (MRI) of human brain scans. The pipeline of the proposed method makes use of filters such as trimmed mean and top-hat to blur and highlight the hippocampal...
Main goal of this paper is a detailed analysis of the performances of Random Forest algorithm in the field of automated hippocampalsegmentation using 3D MR Images. Fifty-six T1-weighted whole brain MR images were included in the study, together with the related manually segmented bilateral hippocampi (mask). Firstly, the relationship between manual and automated segmentations of hippocampus was explored...
Automatic anatomical segmentation of pediatric brain MR data sets can be pursued with the use of registration algorithms when segmentation priors (atlases) are in hand. We investigated the performance of a maximum probability pediatric atlas (MPPA), template based registration and label propagation. The MPPA was created from the 33 pediatric data sets, available through www.brain-development.org....
We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial...
High-resolution Magnetic resonance imaging (MRI) is helpful in diagnosing diseases such as schizophrenia, alzheimer, dementia etc. Brain segmentation is an important preprocess in medical imaging applications. In this study we compare atlas based segmentation and manual segmentation of hippocampus for volumetric measures. A statistically difference was obtained between automatic and manual measurement...
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