Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology

Morawski, Markus; Kirilina, Evgeniya; Scherf, Nico; Jäger, Carsten; Reimann, Katja; Trampel, Robert; Gavriilidis, Filippos; Geyer, Stefan; Biedermann, Bernd; Arendt, Thomas; Weiskopf, Nikolaus

doi:10.1016/j.neuroimage.2017.11.060

Cited by 89 publications

(81 citation statements)

References 56 publications

(91 reference statements)

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“…One possible avenue is to perform histology on selected excised samples after the scan which could provide a ground-truth, several methods exist to perform such histology analysis. CLARITY is a method using optical 3D imaging combined with a tissue clearing method which can provides neuron density, fiber orientation distribution and cell type classification [53]. X-ray microscopy is an instrument that can generate an entire 3D view of the interior of otherwise optically opaque samples in a non-destructive way [54].…”

Section: Ground Truth Validationmentioning

confidence: 99%

Decoding the microstructural properties of white matter using realistic models

Hédouin

Metere

Chan

et al. 2020

Preprint

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The multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons in respect to the main static field. Although analytical solutions, based on the Hollow Cylinder Model have been able to predict some of the behaviour the hollow cylinder model, it has been shown that realistic models of white matter offer a better description of the signal behaviout observed.In this work, we present a pipeline to (i) generate realistic 2D white matter models with its microstructure based on real axon but with arbitrary fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D models can be used to simulate an MR signal that provides a good approximation of the signal obtained from a real 3D white matter model obtained using electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if multi-echo multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A Deep Learning Network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex-vivo dataset acquired in 9-orientations in respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics.[Highlights] -A pipeline to generate realistic white matter models of arbitrary fiber volume fraction and g-ratio is presented; -We present a methodology to simulated the gradient echo signal from segmented 2D and 3D models of white matter, which takes into account the interaction of the static magnetic field with the anisotropic susceptibility of the myelin phospholipids; -Deep Learning Networks can be used to decode microstructural white matter parameters from the signal of multi-echo multi-orientation data;

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Section: Ground Truth Validationmentioning

confidence: 99%

Decoding the microstructural properties of white matter using realistic models

Hédouin

Metere

Chan

et al. 2020

Preprint

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“…2, 4 document this). A recent series of publications summarizes this scanner-associated resolution deficit under the term microstructure and several strategies suggest to bridge this shortcoming [55][56][57][58]. Likewise, tractography studies should be supplemented by histological protocols [59].…”

Section: The Potential Of High-resolution 7 T Mr Scanners In Analysinmentioning

confidence: 99%

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure

et al. 2019

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Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an everincreasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/ or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI-and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

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“…The entire procedure of case recruitment, acquisition of the patient's personal data, the protocols and the informed consent forms, performing the autopsy, and handling the autoptic material have been approved by the local ethics committee. For details on tissue preparation and clearing see [10]. …”

Section: Sample Preparationmentioning

confidence: 99%

Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics

Thierbach

Bazin

Back

et al. 2018

Machine Learning in Medical Imaging

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Abstract. Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmentation of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.

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Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology

Cited by 89 publications

References 56 publications

Decoding the microstructural properties of white matter using realistic models

Decoding the microstructural properties of white matter using realistic models

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure

Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics

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