Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms

Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four different registration tasks. The experimental results show the efficacy of suggesting annotations according to their informativeness, and an improved capacity to assess the quality of the outputs of registration algorithms. In addition, our approach yields, from sparse annotations only, a dense visualization of the errors made by a registration method. The source code of our approach supporting both 2D and 3D data is publicly available at https://github.com/LoicPeter/evaluation-deformable-registration

Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21

Published in final edited form as: Brain Struct Funct. 2019 January ; 224(1): 351–362. doi:10.1007/s00429-018-1777-z.Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.Accepted manuscrip

Label-free, multi-scale imaging of ex-vivo mouse brain using spatial light interference microscopy

Brain connectivity spans over broad spatial scales, from nanometers to centimeters. In order to understand the brain at multi-scale, the neural network in wide-field has been visualized in detail by taking advantage of light microscopy. However, the process of staining or addition of fluorescent tags is commonly required, and the image contrast is insufficient for delineation of cytoarchitecture. To overcome this barrier, we use spatial light interference microscopy to investigate brain structure with high-resolution, sub-nanometer pathlength sensitivity without the use of exogenous contrast agents. Combining wide-field imaging and a mosaic algorithm developed in-house, we show the detailed architecture of cells and myelin, within coronal olfactory bulb and cortical sections, and from sagittal sections of the hippocampus and cerebellum. Our technique is well suited to identify laminar characteristics of fiber tract orientation within white matter, e.g. the corpus callosum. To further improve the macro-scale contrast of anatomical structures, and to better differentiate axons and dendrites from cell bodies, we mapped the tissue in terms of its scattering property. Based on our results, we anticipate that spatial light interference microscopy can potentially provide multiscale and multicontrast perspectives of gross and microscopic brain anatomy.ope

UV-fluoerscence spectroscopy for identification of varnishes in works of art: influence of the underlayer on the emission spectrum

UV-fluoerscence spectroscopy for identification of varnishes in works of art: influence of the underlayer on the emission spectru

as-PSOCT: Volumetric microscopic imaging of human brain architecture and connectivity.

Published in final edited form as: Neuroimage. 2018 January 15; 165: 56–68. doi:10.1016/j.neuroimage.2017.10.012.Polarization sensitive optical coherence tomography (PSOCT) with serial sectioning has enabled the investigation of 3D structures in mouse and human brain tissue samples. By using intrinsic optical properties of back-scattering and birefringence, PSOCT reliably images cytoarchitecture, myeloarchitecture and fiber orientations. In this study, we developed a fully automatic serial sectioning polarization sensitive optical coherence tomography (as-PSOCT) system to enable volumetric reconstruction of human brain samples with unprecedented sample size and resolution. The 3.5 μm in-plane resolution and 50 μm through-plane voxel size allow inspection of cortical layers that are a single-cell in width, as well as small crossing fibers. We show the abilities of as-PSOCT in quantifying layer thicknesses of the cerebellar cortex and creating microscopic tractography of intricate fiber networks in the subcortical nuclei and internal capsule regions, all based on volumetric reconstructions. as-PSOCT provides a viable tool for studying quantitative cytoarchitecture and myeloarchitecture and mapping connectivity with microscopic resolution in the human brain.U01 MH093765 - NIMH NIH HHS; R01 NS070963 - NINDS NIH HHS; U01 NS086625 - NINDS NIH HHS; R21 EB018907 - NIBIB NIH HHS; R01 AG016495 - NIA NIH HHS; S10 RR019307 - NCRR NIH HHS; R01 NS052585 - NINDS NIH HHS; R01 AG008122 - NIA NIH HHS; R01 AG049899 - NIA NIH HHS; R01 EB019956 - NIBIB NIH HHS; R21 NS072652 - NINDS NIH HHS; P01 NS055104 - NINDS NIH HHS; S10 RR023043 - NCRR NIH HHS; K01 DK101631 - NIDDK NIH HHS; R01 EB006758 - NIBIB NIH HHS; P41 EB015896 - NIBIB NIH HHS; R01 NS083534 - NINDS NIH HHS; S10 RR023401 - NCRR NIH HHShttps://www.ncbi.nlm.nih.gov/pubmed/29017866https://www.ncbi.nlm.nih.gov/pubmed/29017866Accepted manuscrip

Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21

Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.National Institute of Mental Health (Grant MH107456)National Institute for Biomedical Imaging and Bioengineering (Grant P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956)National Institute on Aging (Grant 5R01AG008122, R01AG016495)National Institute of Diabetes and Digestive and Kidney Diseases (Grant 1-R21-DK-108277-01)National Institute for Neurological Disorders and Stroke (Grant R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625)NIH Blueprint for Neuroscience Research (Grant 5U01-MH093765)NIH Shared Instrumentation (Grants 1S10RR023401, 1S10RR019307, 1S10RR023043

Multispectral and Hyperspectral Imaging for Skin Acquisition and Analysis

International audienceMultispectral and hyperspectral imaging are imaging modalities that collect more physical information than conventional color imaging, allowing detailed study of material properties. Applied to skin, these imaging methods enable noninvasive, pixel-by-pixel surface measurements, making them promising tools for in vivo skin study. In particular, skin spectral images can be analyzed using physics-based models, or artificial intelligence combined with databases. A typical application is the estimation of information such as melanin concentration and total blood volume fraction from a model-based approximation of skin structure and composition and a model of light–skin interaction

Media 2: Holographic laser Doppler imaging of microvascular blood flow

Originally published in JOSA A on 01 December 2014 (josaa-31-12-2723

Media 1: Holographic laser Doppler imaging of microvascular blood flow

Originally published in JOSA A on 01 December 2014 (josaa-31-12-2723

Quantitative assessment of regional variation in tissue clearing efficiency using optical coherence tomography (OCT) and magnetic resonance imaging (MRI): A feasibility study

Tissue clearing has gained attention as a pioneering research tool for imaging of large tissue samples. This technique improves light transmission by reducing light scattering within tissues, either by removing lipids or by replacing water with a high refractive index solution. Although various clearing techniques have been developed, quantitative assessments on clearing efficacy depending on tissue properties are rare. In this study, we developed the quantitative mapping of regional clearing efficacy using mean free path in optical coherence tomography (OCT) and proton density in magnetic resonance imaging (MRI), and demonstrated its feasibility in the brain sample with four representative clearing techniques (benzyl alcohol and benzyl benzoate [BABB], Clear(T), Scale, and passive CLARITY technique [PACT]). BABB (solvent-based clearing), involving both refractive index matching and lipid removal, exhibited best optical clearing performance with the highest proton density reduction both in gray and white matter. Lipid-removing techniques such as Scale (aqueous hyperhydration) and PACT (hydrogel embedding) showed higher clearing efficiency in white matter than gray matter in accordance with larger proton density increase in white matter. For Clear(T) (aqueous-based simple immersion), we observed lowest clearing efficiency in the white matter as well as poor lipid removal reflected in low proton density reduction. Our results showed the feasibility of the regional mapping of clearing efficacy and correlating optical transparency and proton density changes using OCT and MRI from existing tissue clearing techniques. This novel quantitative mapping of clearing efficacy depending on tissue types and clearing methods may be helpful in the development of optimized clearing methods for different biological samples