1,468 research outputs found

    Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

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    Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

    Reduced hippocampal volume in healthy young ApoE4 carriers: an MRI study.

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    The E4 allele of the ApoE gene has consistently been shown to be related to an increased risk of Alzheimer's disease (AD). The E4 allele is also associated with functional and structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess volumes of deep grey matter structures of 22 healthy younger ApoE4 carriers and 22 non-carriers (20-38 years). Volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, thalamus and brain stem were calculated by FMRIB's Integrated Registration and Segmentation Tool (FIRST) algorithm. A significant drop in volume was found in the right hippocampus of ApoE4 carriers (ApoE4+) relative to non-carriers (ApoE4-), while there was a borderline significant decrease in the volume of the left hippocampus of ApoE4 carriers. The volumes of no other structures were found to be significantly affected by genotype. Atrophy has been found to be a sensitive marker of neurodegenerative changes, and our results show that within a healthy young population, the presence of the ApoE4+ carrier gene leads to volume reduction in a structure that is vitally important for memory formation. Our results suggest that the hippocampus may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age. Although volume reductions were noted bilaterally in the hippocampus, atrophy was more pronounced in the right hippocampus. This finding relates to previous work which has noted a compensatory increase in right hemisphere activity in ApoE4 carriers in response to preclinical declines in memory function. Possession of the ApoE4 allele may lead to greater predilection for right hemisphere atrophy even in healthy young subjects in their twenties

    Sub-cortical brain structure segmentation using F-CNN's

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    In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.Comment: ISBI 2016: International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republi

    Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

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    We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929

    A structural magnetic resonance imaging study in therapy-naĂŻve transsexual individuals

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    Background: Transsexuality is explained and defined as a gender-identity disorder, characterised by very strong conviction of belonging to the opposite sex and has been associated with a distinct neuroanatomical pattern. Materials and methods: We performed a structural analysis in search of possible differences in grey matter structures based on magnetic resonance imaging scans of the brains of 26 individuals between 19 and 38 years of age. The participants were divided into two groups of 15 controls and 11 transgender individuals. The segmentation of subcortical grey matter was performed using FIRST model a model-based segmentation/registration tool, from FSL software package. Results: The results showed that the volume of the brain region called nucleus accumbens on the left side was significantly smaller in the group of transgender individuals compared to the control. It was the most important parameter which was shown to make distinction between two examined groups. Conclusions: The results also showed decreased volumes of the left thalamus, right hippocampus and right caudate nucleus

    Gray matter structural correlates of fatigue in multiple sclerosis

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    We aimed to assess whether frontal cortex-striatum-thalamus (FCST) pathway or other grey matter (GM) structures are associated with longitudinal patterns of fatigue, namely reversible (RF) versus sustained fatigue (SF). MS patients enrolled in our prospective cohort were grouped based on their longitudinal Modified Fatigue Impact Scale (MFIS) scores: 1. SF: MFIS≥38 at the two most recent yearly assessments; 2. RF: MFIS<38 at last assessment, but presence of at least one previous MFIS≥38; 3. Never Fatigued (NF): at least five MFIS<38. Accordingly, we selected 98 patients (30 SF, 31 RF, 37 NF; age-range:29-66, female/male:76/22, Extended Disability Status Scale (EDSS)6; 13 patients with secondary progressive (SP) MS and 85 with relapsing remitting (RR) MS in remission). Disability and depression were assessed using the EDSS and CES-D, respectively. 3T T1-weighted MRI was used for voxel based morphometry (VBM) to survey for GM atrophy associated with fatigue, controlling for age, sex and EDSS. Group-wise volumetric comparison was performed on deep GM structures identified by VBM, controlling for age, sex, EDSS and CES-D score. VBM showed significant inverse relation between the MFIS cognitive subscale score and areas within the bilateral fronto-medial and fronto-orbital cortices, anterior striata, thalami, temporal poles, insulae and left lateral occipital cortex (peak FWE-p value of 0.021), and between the MFIS physical subscale and areas within the bilateral frontal poles, and frontal medial cortices (peak FWE-p value of 0.043). Volumetric analysis showed significant atrophy in the putamen (RF<NF p<0.0004; SF<NF p<0.0085) and thalamus (RF<NF p<0.00048)
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