MINC 2.0: A Flexible Format for Multi-Modal Images

Vincent, Robert D.; Neelin, P.; Khalili-Mahani, Najmeh; Janke, Andrew L.; Fonov, Vladimir S.; Robbins, Steven; Baghdadi, Leila; Lerch, Jason P.; Sled, John G.; Adalat, Reza; MacDonald, David; Zijdenbos, Alex P.; Collins, D. Louis; Evans, Alan C.

doi:10.3389/fninf.2016.00035

Cited by 66 publications

(48 citation statements)

References 16 publications

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“…An average template was created from all images in the study using iterative linear and nonlinear image registration as previously described . Pydpiper, MINC tools, and ANTs were used for nonlinear registration. The centroid of each tumor was calculated and rendered using ITK‐SNAP's “Convert3D” tool, which contains a list of functions for 3D image manipulation and format conversion.…”

Section: Methodsmentioning

confidence: 99%

MEMRI‐based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma

Rallapalli

Tan

Volkova

et al. 2019

Magnetic Resonance in Med

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Purpose Genetically engineered mouse models of sporadic cancers are critical for studying tumor biology and for preclinical testing of therapeutics. We present an MRI‐based pipeline designed to produce high resolution, quantitative information about tumor progression and response to novel therapies in mouse models of medulloblastoma (MB). Methods Sporadic MB was modeled in mice by inducing expression of an activated form of the Smoothened gene (aSmo) in a small number of cerebellar granule cell precursors. aSmo mice were imaged and analyzed at defined time‐points using a 3D manganese‐enhanced MRI‐based pipeline optimized for high‐throughput. Results A semi‐automated segmentation protocol was established that estimates tumor volume in a time‐frame compatible with a high‐throughput pipeline. Both an empirical, volume‐based classifier and a linear discriminant analysis‐based classifier were tested to distinguish progressing from nonprogressing lesions at early stages of tumorigenesis. Tumor centroids measured at early stages revealed that there is a very specific location of the probable origin of the aSmo MB tumors. The efficacy of the manganese‐enhanced MRI pipeline was demonstrated with a small‐scale experimental drug trial designed to reduce the number of tumor associated macrophages and microglia. Conclusion Our results revealed a high level of heterogeneity between tumors within and between aSmo MB models, indicating that meaningful studies of sporadic tumor progression and response to therapy could not be conducted without an imaging‐based pipeline approach.

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Section: Methodsmentioning

confidence: 99%

MEMRI‐based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma

Rallapalli

Tan

Volkova

et al. 2019

Magnetic Resonance in Med

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“…All raw images were then preprocessed using the minc-bpipe-library (https://github.com/CobraLab/minc-bpipe-library; Sadedin, Pope, & Oshlack, 2012;Vincent et al, 2016). All raw images were then preprocessed using the minc-bpipe-library (https://github.com/CobraLab/minc-bpipe-library; Sadedin, Pope, & Oshlack, 2012;Vincent et al, 2016).…”

Section: Image Analysismentioning

confidence: 99%

“…All acquired T1-weighted images underwent visual quality assessment and were excluded if excessive motion or scanner artifacts were observed. All raw images were then preprocessed using the minc-bpipe-library (https://github.com/CobraLab/minc-bpipe-library; Sadedin, Pope, & Oshlack, 2012;Vincent et al, 2016). This pipeline uses a "clean_and_center" stage to uniformize the direction of cosines and the zero-point of the scan to the center of the image, followed by a bias field correction for contrast inhomogeneity using the N4ITK algorithm (Tustison et al, 2010), and a brain extraction step to isolate the brain from nonbrain tissues based on nonlocal segmentation technique (BEaST; Eskildsen et al, 2012).…”

Section: Image Analysismentioning

confidence: 99%

Hippocampal alterations and functional correlates in adolescents and young adults with congenital heart disease

Fontes

Rohlicek

Saint‐Martin

et al. 2019

Human Brain Mapping

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There is a high prevalence of neurodevelopmental impairments in individuals living with congenital heart disease (CHD) and the neural correlates of these impairments are not yet fully understood. Recent studies have shown that hippocampal volume and shape differences may provide unique biomarkers for neurodevelopmental disorders. The hippocampus is vulnerable to early life injury, especially in populations at risk for hypoxemia or hemodynamic instability such as in neonates with CHD. We compared hippocampal gray and white matter volume and morphometry between youth born with CHD (n = 50) aged 16–24 years and healthy peers (n = 48). We also explored whether hippocampal gray and white matter volume and morphometry are associated with executive function and self‐regulation deficits. To do so, participants underwent 3T brain magnetic resonance imaging and completed the self‐reported Behavior Rating Inventory of Executive Function—Adult version. We found that youth with CHD had smaller hippocampal volumes (all statistics corrected for false discovery rate; q < 0.05) as compared to controls. We also observed significant smaller surface area bilaterally and inward displacement on the left hippocampus predominantly on the ventral side (q < 0.10) in the CHD group that were not present in the controls. Left CA1 and CA2/3 were negatively associated with working memory (p < .05). Here, we report, for the first‐time, hippocampal morphometric alterations in youth born with CHD when compared to healthy peers, as well as, structure–function relationships between hippocampal volumes and executive function. These differences may reflect long lasting alterations in brain development specific to individual with CHD.

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“…(https://github.com/thomshaw92/nifti_normalise), a nifti implementation of 'mincnorm' from the medical imaging network common (MINC) data toolkit (Vincent et al, 2016) that normalises image intensities between two percent-critical thresholds, removing outlying intensity values V. If multiple repetitions of the dedicated T2w scan are available, non-linear realignment of these scans to reduce motion artefacts and increase the sharpness of the scans as in Shaw et al (2019).…”

Section: Preprocessing and Cross Sectional Processingmentioning

confidence: 99%

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI

Shaw

York

Ziaei

et al. 2019

Preprint

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AbstractThe volumetric and morphometric examination of hippocampus formation subfields in a longitudinal manner using in vivo MRI could lead to more sensitive biomarkers for neuropsychiatric disorders and diseases including Alzheimer’s disease, as the anatomical subregions are functionally specialised. Longitudinal processing allows for increased sensitivity due to reduced confounds of inter-subject variability and higher effect-sensitivity than cross-sectional designs. We examined the performance of a new longitudinal pipeline (Longitudinal Automatic Segmentation of Hippocampus Subfields [LASHiS]) against three freely available, published approaches. LASHiS automatically segments hippocampus formation subfields by propagating labels from cross-sectionally labelled time point scans using joint-label fusion to a non-linearly realigned ‘single subject template’, where image segmentation occurs free of bias to any individual time point. Our pipeline measures tissue characteristics available in in vivo high-resolution MRI scans, at both clinical (3 Tesla) and ultra-high field strength (7 Tesla) and differs from previous longitudinal segmentation pipelines in that it leverages multi-contrast information in the segmentation process. LASHiS produces robust and reliable automatic multi-contrast segmentations of hippocampus formation subfields, as measured by higher volume similarity coefficients and Dice coefficients for test-retest reliability and robust longitudinal Bayesian Linear Mixed Effects results at 7 T, while showing sound results at 3 T. All code for this project including the automatic pipeline is available at https://github.com/CAIsr/LASHiS

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MINC 2.0: A Flexible Format for Multi-Modal Images

Cited by 66 publications

References 16 publications

MEMRI‐based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma

MEMRI‐based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma

Hippocampal alterations and functional correlates in adolescents and young adults with congenital heart disease

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI

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