scholarly journals An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kelly Payette ◽  
Priscille de Dumast ◽  
Hamza Kebiri ◽  
Ivan Ezhov ◽  
Johannes C. Paetzold ◽  
...  
Keyword(s):  
Grey Matter ◽  
Brain Volume ◽  
Fetal Brain ◽  
Fetal Tissue ◽  
Brain Segmentation ◽  
Human Fetal Brain ◽  
Tissue Segmentation ◽  
Segmentation Algorithms ◽  
Deep Grey Matter ◽  
Open Datasets

AbstractIt is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.

scholarly journals Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mina Rizkallah ◽  
Mohamed Hefida ◽  
Mohamed Khalil ◽  
Rasha Mahmoud Dawoud
Keyword(s):  
Multiple Sclerosis ◽  
Statistical Analysis ◽  
White Matter ◽  
Grey Matter ◽  
Brain Volume ◽  
Correlation Coefficients ◽  
White Matter Lesions ◽  
The Other ◽  
Automated Segmentation ◽  
Deep Grey Matter

Abstract Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores. Results All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35). Conclusions The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.

scholarly journals Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

2021 ◽  
pp. 1-1
Author(s):  
Yue Sun ◽  
Kun Gao ◽  
Zhengwang Wu ◽  
Guannan Li ◽  
Xiaopeng Zong ◽  
...  
Keyword(s):  
Brain Segmentation ◽  
Segmentation Algorithms ◽  
Infant Brain

Deep grey matter iron accumulation in alcohol use disorder

NeuroImage ◽  
2017 ◽  
Vol 148 ◽  
pp. 115-122 ◽  
Author(s):  
Michal Juhás ◽  
Hongfu Sun ◽  
Matthew R.G. Brown ◽  
Marnie B. MacKay ◽  
Karl F. Mann ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Grey Matter ◽  
Iron Accumulation ◽  
Deep Grey Matter

scholarly journals Quantitative analysis of normal fetal brain volume and flow by three-dimensional power Doppler ultrasound

2013 ◽  
Vol 76 (9) ◽  
pp. 504-509 ◽  
Author(s):  
Ju-Chun Hsu ◽  
Yi-Cheng Wu ◽  
Peng-Hui Wang ◽  
Hsing-I Wang ◽  
Chi-Mou Juang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Doppler Ultrasound ◽  
Brain Volume ◽  
Power Doppler ◽  
Three Dimensional ◽  
Fetal Brain ◽  
Power Doppler Ultrasound

Imaging Normal and Abnormal Brain Development: New Perspectives for Child Psychiatry

2001 ◽  
Vol 35 (3) ◽  
pp. 272-281 ◽  
Author(s):  
Judith L. Rapoport ◽  
Xavier F. Castellanos ◽  
Nitin Gogate ◽  
Kristin Janson ◽  
Shawn Kohler ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Brain Development ◽  
Attention Deficit ◽  
Grey Matter ◽  
Brain Volume ◽  
Brain Magnetic Resonance Imaging ◽  
Childhood And Adolescence ◽  
Hyperactivity Disorder ◽  
Developmental Neuroscience ◽  
Abnormal Brain

Objective: The availability of non-invasive brain imaging permits the study of normal and abnormal brain development in childhood and adolescence. This paper summarizes current knowledge of brain abnormalities of two conditions, attention deficit hyperactivity disorder (ADHD) and childhood onset schizophrenia (COS), and illustrates how such findings are bringing clinical and preclinical perspectives closer together. Method: A selected review is presented of the pattern and temporal characteristics of anatomic brain magnetic resonance imaging (MRI) studies in ADHD and COS. These results are discussed in terms of candidate mechanisms suggested by studies in developmental neuroscience. Results: There are consistent, diagnostically specific patterns of brain abnormality for ADHD and COS. Attention deficit hyperactivity disorder is characterized by a slightly smaller (4%) total brain volume (both white and grey matter), less-consistent abnormalities of the basal ganglia and a striking (15%) decrease in posterior inferior cerebellar vermal volume. These changes do not progress with age. In contrast, patients with COS have smaller brain volume due to a 10% decrease in cortical grey volume. Moreover, in COS there is a progressive loss of regional grey volume particularly in frontal and temporal regions during adolescence. Conclusions: In ADHD, the developmental pattern suggests an early non-progressive ‘lesion’ involving neurotrophic factors controlling overall brain growth and selected dopamine circuits. In contrast, in COS, which shows progressive grey matter loss, various candidate processes influencing later synaptic and dendritic pruning are suggested by human post-mortem and developmental animal studies.

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