High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI

Pardoe, Heath R.; Antony, Arun; Hetherington, Hoby P.; Bagić, Anto; Shepherd, Timothy M.; Friedman, Daniel; Devinsky, Orrin; Pan, Jullie W.

doi:10.1002/hbm.25348

Cited by 6 publications

(2 citation statements)

References 44 publications

(42 reference statements)

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“…Through the use of fMRI with 1.0 mm × 1.0 mm resolution, activation could be localized to subfields of anterior CA2 and CA3 during learning and posterior CA2 and CA1 during retrieval BOLD VASO of novel associations (77). Pardoe et al conducted automatic segmentation for the hippocampus and amygdala on whole-brain MP2RAGE images with 700-μm isotropic resolution, acquired at 7T using a 3D convolutional neural network (78). The results showed high concordance with those of manual volumetry.…”

Section: The Hippocampusmentioning

confidence: 99%

Neuroimaging at 7 Tesla: a pictorial narrative review

Okada¹,

Fujimoto²,

Fushimi³

et al. 2022

Quant Imaging Med Surg

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Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution.Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

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Section: The Hippocampusmentioning

confidence: 99%

Neuroimaging at 7 Tesla: a pictorial narrative review

Okada¹,

Fujimoto²,

Fushimi³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Deep learning has been used for brain image segmentation ( Greve et al, 2021 ; Pardoe et al, 2021 ; Svanera et al, 2021 ; Balboni et al, 2022 ), but it differs from conventional approaches in that it learns non-linear mappings at both intensity and semantic levels in an end-to-end manner with no need of utilizing hand-crafted features. Deep learning-based segmentation methods have been found to improve over conventional approaches by considerable margins in terms of both segmentation accuracy and inference efficiency ( Dolz et al, 2018 ; Brusini et al, 2020 ; Wu and Tang, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches

Lyu,

Bartlett,

Nasrallah

et al. 2023

Front. Neurosci.

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The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test–retest 3T and 7T sessions were used to extensively assess the test–retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

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High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI

Pardoe¹,

Antony

Hetherington

et al. 2021

Human Brain Mapping

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Image labeling using convolutional neural networks (CNNs) are a template‐free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty‐one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN‐based and manual labels. A leave‐one‐out cross validation scheme was used. CNN‐based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN‐based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN‐based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN‐based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub‐mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

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High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI

Cited by 6 publications

References 44 publications

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging at 7 Tesla: a pictorial narrative review

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches

High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI

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