Journal: bioRxiv

Article Title: Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

doi: 10.1101/2020.06.30.181255

Figure Lengend Snippet: (a) Comparative registration accuracy (STP data of intact brain) by four different registration methods, aMAP, ClearMap, MIRACL and our BIRDS. (b) Comparative registration accuracy (LSFM data of clarified brain) by four methods. Magnified views of four region-of-interests ( a1 - a4 , b1 - b4 , blue boxes) selected from the horizontal (left, top) and coronal planes (left, bottom) are shown in the right four columns, to three-dimensionally detail the registration/annotation accuracy by each method. All comparative annotation results are directly outputted from the programs without manual correction. Scale bar, 1mm (whole-brain view), and 250 μm (magnified view). (c) Ten groups of 3D fiducial points of interest (POIs) manually identified across the 3D space of whole brains. The blue and red points belong to the fixed experimental image, and the registered Allen template image, respectively. The ten POIs are selected from following landmarks: POIs : cc1 : corpus callosum, midline; acoL , acoR : anterior commisure, olfactory limb; CPL, CPR : Caudoputamen, Striatum dorsal region; cc2 : corpus callosum, midline; cc3 : corpus callosum, midline; MM : medial mammillary nucleus, midline; DGsgL , DGsgR : dentate gyrus, granule cell layer. The registration error by each method can be thereby quantified through measuring the Euclidean distance between each pair of POIs in the experimental image and template image. (d) Box diagram comparing the POIs distances of five brains registered by four methods. Brain 1, 2: STPT images of two intact brains. Brain 1 is also shown in (a) . Brain 3, 4, 5: LSFM images of three clarified brains (u-DISCO) which show significant deformations. Brain 5 is also shown in (b) . The median error distance of 50 pairs of POIs in the 5 brains registered by BIRDS is ~104 μm, which is compared to ~292 μm by aMAP, ~ 204 μm by ClearMap, and ~151 μm by most recent MIRACL. (e), (f) Comparative plot of Dice scores in nine registered regions of the five brains. The results are grouped by brains in (e) , and regions in (f) . The calculation is implemented at single nuclei level. When the results are analyzed by brains, BIRDS surpass the other 3 methods most on LSFM dataset #5, with 0.881 median Dice score being compared to 0.574 by aMAP, 0.72 by ClearMap, and 0.645 by MIRACL. At the same time, all the methods perform well on STP dataset #2 with median Dice of 0.874 by aMAP, 0.92 by ClearMap, 0.872 by MIRACL, and 0.933 by our BIRDS. When the results are compared by 9 functional regions, the median values acquired by our BIRDS were also higher than the other three methods. Even the lowest median Dice score by our method is still 0.799 (indicated by black line), which is notably higher than 0.566 by aMAP, 0.596 by ClearMap, and 0.722 by MIRACL.

Article Snippet: Thanks to the 3D digital map generated by BIRDS pipeline, BIRDS-Imaris can process each segmented brain area separately, namely calculating the parameters for cell recognition algorithm using local image statistics instead of processing the whole-brain image stack at once.

Techniques: Functional Assay

Journal: bioRxiv

Article Title: Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

doi: 10.1101/2020.06.30.181255

Figure Lengend Snippet: (a) Rendered 3D digital atlas of the whole brain (a2, Pseudo color), which is generated from the registered template and annotation files (a1, overlay of annotation mask and image data). (b) Interactive hierarchical tree shown as a sidebar menu in the BIRDS program indexing the name of brain regions annotated in CCFv3. Clicking on any annotation name in the side bar of the hierarchal tree highlights the corresponding structure in 3D brain map (b1, b2), and vice versa. For example, brain region LP was highlighted in the space after its name was chosen in the menu (b1). 3D rendering of the individual brain after applying the deformation field in reverse to a whole brain surface mask. 3D digital atlas (CCFv3) is shown on the left; the individual brain is shown on the right (b2). (c) The distribution of axonal projections of five single neurons in 3D map space. The color-rendered space shown in horizontal, sagittal, and coronal views highlights multiple areas in the telencephalon, anterior cingulate cortex, striatum, and amygdala, which are all potential target areas of layer-2/3 neurons projections. (d) The traced axons of five selected neurons are shown in (N=5). ENTm, Entorhinal area, medial part, dorsal zone; RSPagl, Retrosplenial area, lateral agranular part; VIS, Visual areas; SSp-bfd, Primary somatosensory area, barrel field; AUDd, Dorsal auditory area; AUDpo, Posterior auditory area; TEa, Temporal association areas; CP, Caudoputamen; IA, Intercalated amygdalar nucleus; LA, Lateral amygdalar nucleus; BLA, Basolateral amygdalar nucleus; CEA, Central amygdalar nucleus; ECT, Ectorhinal area. (e) Quantification of the projection strength across the targeting areas of the five GFP-labelled neurons. The color code reflects the projections strengths of each neuron, determined as axon length per target area, normalized to the axon length in VIS.

Article Snippet: Thanks to the 3D digital map generated by BIRDS pipeline, BIRDS-Imaris can process each segmented brain area separately, namely calculating the parameters for cell recognition algorithm using local image statistics instead of processing the whole-brain image stack at once.

Techniques: Generated

Journal: bioRxiv

Article Title: Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

doi: 10.1101/2020.06.30.181255

Figure Lengend Snippet: (a) Cell counting of retrogradely labelled striatum-projecting cells. We selected two volumes (1×1×1 mm 3 ) from SS and VIS areas, respectively, to show the difference in cell density and the quantitative results by BIRDS-Imaris and conventional Imaris. Here, separate quantification parameters set for different brain areas in BIRD-Imaris procedure lead to obviously more accurate counting results. Scale bar, 2 mm. (b) 3D-rendered images of labelled cells in the whole brain space, shown in horizontal, sagittal, and coronal views. The color rendering of the cell bodies is in accordance with the CCFv3, and the cells are mainly distributed in Isocortex (darker hue). (c) The cell density calculated for 20 brain areas. The cell densities of MO and SS are highest ((MO= 421.80 mm −3 ; SS=844.71 mm −3 ) among all the areas. GU, Gustatory areas; TEa, Temporal association areas; AI, Agranular insular area; PL, Prelimbic area; PERI, Perirhinal area; RSP, Retrosplenial area; ECT, Ectorhinal area; ORB, Orbital area; VISC, Visceral area; VIS, Visual areas; MO, Somatomotor areas; SS, Somatosensory areas; AUD, Auditory areas; HPF, Hippocampal formation; OLF, Olfactory areas; CTXsp, Cortical Subplate; STR, Striatum; PAL, Pallidum; BS, Brainstem; CB, Cerebellum. (d) Comparison of the cell numbers by three different counting methods, BIRDS, Imaris (3D whole brain directly), and manual counting (2D slice by slice for whole brain). (e) The cell counting accuracy by BIRDS-Imaris (orange) and conventional Imaris methods (blue), referring to the manual counting. Besides highly diverged accuracy for the 20 regions, the counting results by conventional Imaris in STR and BS regions are especially inaccurate.

Article Snippet: Thanks to the 3D digital map generated by BIRDS pipeline, BIRDS-Imaris can process each segmented brain area separately, namely calculating the parameters for cell recognition algorithm using local image statistics instead of processing the whole-brain image stack at once.

Techniques: Cell Counting, Comparison

Journal: Scientific Reports

Article Title: Digital restoration of colour cinematic films using imaging spectroscopy and machine learning

doi: 10.1038/s41598-022-25248-5

Figure Lengend Snippet: The conventional digital restoration strategy. ( a ) Comparison of RGB images before and after the hand restoration achieved by DaVinci Resolve 17 . ( b ) Illustration of the processing pipelines. ( c ) Comparison of the histograms before and after restoration with the reference S1.

Article Snippet: Figure 3 The conventional digital restoration strategy. ( a ) Comparison of RGB images before and after the hand restoration achieved by DaVinci Resolve 17 . ( b ) Illustration of the processing pipelines. ( c ) Comparison of the histograms before and after restoration with the reference S1.

Techniques: