dataesr/scientific-paragraphs-categorization huggingface dataset:
In both EM volumes, phagosomes were identified as completely internalized, membrane delimited structures. Lysosomes appeared electron dense and measured ~ 500 nm in diameter (Fig. 2b ), while PLs were multichambered, large organelles measuring ~ 750 nm (Fig. 2e -n, Extended Data Video 2). Seldom visualized in large numbers by EM, PLs represents the final step of phagocytosis, and constitute a highly acidic compartment capable of destroying ingested elements  26, 27. Mapping the distribution of PLs in one OPC (Fig. 2o ) demonstrated that they were widely distributed within the cell, but particularly prevalent near the tips of OPC processes. This finding led us to manually quantify PLs in eight more 3D reconstructed OPCs in P36 dataset for comparison. PLs in OPCs were large enough to visualize their internal contents.
The presence of abundant PLs in OPCs containing synaptic material was unexpected, as most structural pruning of neurons is thought to be mediated by microglia  2, 28. To determine the relative abundance of PLs in these two glial cell types, we quantified PLs in complete 3D reconstructions of microglia in the same volumes. This analysis revealed that PLs were significantly more abundant in OPCs than microglia at this age (Fig. 2o ,p;Extended Data Fig. 2 ) (Total PLs/cell: OPCs, 108 ± 48; microglia, 9 ± 3; student's t-test, p < 0. 001, n = 9); although OPCs are larger than microglia, the density of PLs was still significantly higher in OPCs (Fig. 2q ; Extended data Fig. 2f, g ) (PL density: OPCs, 0. 143 ± 0. 05 PLs/µm 3 ; microglia, 0. 03 ± 0. 01 PLs/µm 3 ; p<0. 001, n = 9), suggesting that OPCs are more actively engaged in neuronal process engulfment at this age.
This structural remodeling of cortical connections declines rapidly after the first postnatal month  2, 33  , coinciding with the end of the critical period of ocular dominance plasticity in the visual cortex. To determine if PLs in OPCs follow a similar developmental progression, we quantified PLs in OPCs reconstructed from serial EM volumes obtained in the P49 mouse visual cortex. Significantly fewer PLs were present in OPCs at this age, despite the similar volume occupied by these cells (Extended Data Fig. 3e , f, g) (PL density: P36 OPCs, 0. 143 ± 0. 05 PLs/µm 3 ; P49 OPCs, 0. 085 ± 0. 03 PLs/µm 3 ; p = 0. 01, student's t-test, n = 9, 11, respectively). Together, these results indicate there is a temporal shift in OPC behavior over time and that OPCs may contribute to an early phase of structural remodeling of neurons during the final phase of the critical period in the visual cortex.
OPCs undergo dramatic changes in gene expression and morphology as they differentiate into myelin forming oligodendrocytes  8  (Extended Data Fig. 3a-d ). To determine if axonal engulfment is preferentially associated with the OPC progenitor state, we also examined the cytosol of premyelinating and mature oligodendrocytes at P49 (due to the smaller volume, there was only one partially reconstructed premyelinating oligodendrocyte in the P36 dataset). These more mature oligodendroglia were distinguished from OPCs by the lack of primary cilia  34  , as well as the presence of sheets of membrane extending along axons and membrane wraps indicative of nascent myelination (Extended Data Fig. 3a-d ). Premyelinating oligodendrocytes had lower densities of PLs than OPCs PL density: P49 OPCs, 0. 085 ± 0.
OPCs prune axons in the mouse cortex The appearance of clusters of 40 nm clear vesicles inside OPC PLs suggests that presynaptic terminals or portions of axons containing terminals are engulfed by these cells. If this process is frequent, OPCs engaged in various stages of this process should be visible in the EM datasets. Indeed, 3D reconstructions revealed that terminal branches of axon collaterals were often surrounded by OPC processes (Fig. 4a-f ). The complex anatomy of the branches and the need to accurately manually annotate and quantify the ingestions, made this analysis particularly challenging. Therefore, two individual main branches from two distinct OPCs in the P36 dataset were examined in detail (OPCs #1, #7) (Extended Data Fig. 1c ,k; Extended data Fig. 6a-h ). We categorized the ingestions as phagosomes (PS), PLs or axon engulfment (Fig.
Furthermore, the transcriptomics data suggests that OPCs are non-discriminatory phagocytes and engulf all neuron types, based on all layers of the cortex. The association of OPCs with terminal and collateral axon branches was observed in all OPCs from both P36 and P49 datasets, indicating that axon pruning is a conserved function of OPCs during this period. Such associations may represent the predecessor to axonal engulfment and suggest that the terminal portion of axon collaterals are targeted for removal by OPCs.
EM Datasets We used two densely segmented and reconstructed volumes, obtained from serial section electron microscopy (ssEM) in mouse primary visual cortex. These data sets were collected as part of the IARPA Machine Intelligence from Cortical Network (MICrONS ) consortium project (https://www. iarpa. gov/index. php/research-programs/microns) to study the functional connectivity of neurons, however they also provide in rich and unprecedented detail the fine morphology of glial cell types. Here we use these datasets to evaluate the population of OPCs and microglia in the 3D environment of the mouse brain visual cortex (preparation methods described below). Dataset 1 was taken from a 200 µm thick sample containing layer 2/3 of mouse visual cortex (P36) and measured approximately 250 µm x 140 µm x 90 µm (Fig.
1, and the imaging point-spread function was measured with 500 nm beads and was approximately 0. 5 ✕ 0. 5 ✕ 3 µm3 in x, y, and z. To aid in registration of optical physiology data to EM data, a wide field image of the cranial window visualizing the surface vasculature was provided in addition to a volumetric image stack of the vasculature, encompassing the region of tissue where the neurophysiology dataset was acquired. The vasculature was imaged by subcutaneous injection of 60 µL 2. 5% Texas Red 3000MW Lysine fixable (Life Technologies D3328, allowing blood vessels and GCaMP6-expressing cell bodies to be imaged simultaneously by 2-photon microscopy. Mice were then transferred to the Allen Institute in Seattle and kept in a quarantine facility for 1 to 3 days, prior to perfusion.
This region was then prepared for Electron Microscopy. Details of the procedures to carefully map the neurophysiology site in the histological sections is described in (details in  9  ). We omitted these details here as no neurophysiology data was used in this manuscript, even though the anatomical data originates from the same block of tissue that was recorded with two-photon imaging. The brain was washed in CB (0. 1 M cacodylate buffer pH 7. 4) and embedded in 2% agarose. The agarose was trimmed and mounted for coronal sectioning in a Leica VT1000S vibratome; successive 200 μm thick slices were taken until the entire region of cortical tissue previously demarcated by manual markings was sectioned. During this procedure, we also acquired blockface images of each brain slice. The coronal sections containing the imaged site were then selected for histological processing (see below).
Proofreading and Annotation of Volumetric Imagery Data We used a combination of Neuroglancer (Maitin-Shepard, https://github. com/google/neuroglancer) and custom tools to annotate and store labeled spatial points  64. In brief, we used Neuroglancer to simultaneously visualize the imagery and segmentation of the 3d EM data. A custom branch of Neuroglancer was developed that could interface with a "dynamic" segmentation database, allowing users to correct errors (i. e. , either merging or splitting neurons) in a centralized database from a web browser. Neuroglancer has some annotation functionality, allowing users to place simple annotations during a session, but does not offer a way to store them in a central location for analysis. We thus built a custom cloud-based database system to store arbitrary annotation data centered associated with spatial points that could be propagated dynamically across proofreading events.
Visualization and Analysis of Mesh Data Neuronal meshes were computed by Igneous (https://github. com/seung-lab/igneous) and kept up to date across proofreading. Meshes were analyzed in a custom python library, MeshParty (https://github. com/sdorkenw/MeshParty), that extends Trimesh (https://trimsh. org) with domainspecific features and VTK (https://www. vtk. org) integration for visualization. In cases where skeletons were used, we computed them with a custom modification of the TEASAR algorithm  65  on the vertex adjacency graph of the mesh object implemented as part of MeshParty. In order to associate annotations such as synapses or AIS boundary points with a mesh, we mapped point annotations to the closest mesh vertex after removing artifacts from the meshing process.
Annotation of cells in the EM volume We used a combination of Neuroglancer (Maitin-Shepard https://github. com/google/neuroglancer) and custom tools to annotate and store labeled spatial points. In brief, we used Neuroglancer to simultaneously visualize the imagery and segmentation of the 3d EM data. Neuroglancer incorporates the capability to store and remark on xyz points within the data. These spatial points and their associated data (e. g. , Cell type, cell body ID number, or organelles) were linked to stored spreadsheets and documents.
Imaging and analysis of primary OPC cultures Cells from three separate culture experiments were used for the analysis. All images were taken at 63x magnification using the AiryScan function on a Zeiss 710 confocal microscope. Airyscanprocessed z-stacks of NG2 + individual OPCs were projected using the maximum intensity projection function, and the number of LAMP2 + vesicles within cell processes were quantified. Molecular analysis of OPCs and microglia with single nucleus RNA-seq and single nucleus DNA methyl-seq2 dataset. We analyzed recently described Chromium 10x V3 single nucleus RNA-seq and single nucleus DNA methyl-seq2 datasets from mouse primary motor cortex  35  , available from the Neuroscience Multi-omic Data Archive (NeMO, https://assets. nemoarchive. org/dat-ch1nqb7). Only the 159,738 nuclei from the dataset generated by the Broad Institute were used.
For gene expression dot plots, the mean UMIs per cluster and proportion of cluster with UMIs greater than 1 were calculated using the scrattch. hicat v0. 0. 22 library (https://github. com/AllenInstitute/scrattch. hicat) and visualized using ggplots2 v3. 3. 3 library 66 in R v3. 4. 1. Mean expression was scaled from 0 to 1 for each gene for each major cell class (i. e. non-neuronal and neurons). To find neuronal subclass marker genes, we first created a Seurat 67 ,  68  object of only the neuronal cell types, downsampled to 500 nuclei per neuronal subclass. We then used the FindAllMarkers function from Seurat v3. 2. 0 with the 'roc' test to identify differentially expressed genes that were enriched in a particular neuronal subclass compared to other neurons. Neuronal subclass marker genes were then filtered to include genes with greater than 2 log2FC in OPCs and microglia relative to each mature oligodendrocyte cluster (Oligo Opalin_1-3), and with greater than 2 log2 cpm expression in OPCs and microglia.
Acknowledgments We thank  John Philips ,  Sill Coulter  and the  Program Management team  at the  AIBS  for their guidance for project strategy and operations. We thank  Hongkui Zeng ,  Christof Koch  and  Allan Jones  for their support and leadership. We thank  Andreas Tolias ,  Jacob Reimer  and their teams at the  Baylor College of Medicine  for providing the mice used for electron microscopy. We thank the  Manufacturing and Processing Engineering team at the AIBS  for their help in implementing the EM imaging and sectioning pipeline. We thank  Rob Young  and the  Technology team at the AIBS  for their work in the imaging processing of the EM data. We thank  Brian Youngstrom ,  Stuart Kendrick  and the  Allen Institute IT team  for support with infrastructure, data management and data transfer.
We thank the  Facilities, Finance , and Legal teams at the  AIBS  for their support on the MICrONS contract. We thank  Stephan Saalfeld  for help with the parameters for 2D stitching and rough alignment of the dataset. We would like to thank the  "Connectomics at Google" team  for developing Neuroglancer and computational resource donations. We also would like to thank  Amazon  and  Intel  for their assistance. We thank  S. Koolman ,  M. Moore ,  S. Morejohn ,  B. Silverman ,  K. Willie , and  R. Willie  for their image analyses,  Garrett McGrath  for computer system administration, and  May Husseini  and  Larry  and  Janet Jackel  for project administration. We thank  Kristina Micheva  and  Stephen J Smith  for advice and feedback. We thank  Prof Joseph Ayers ,  Erin Cram  and  James Monaghan  for guidance, support and feedback.
Code availability All software is open source and available at http://github. com/seung-lab if not otherwise mentioned. Alembic: Stitching and alignment. CloudVolume: Reading and writing volumetric data, meshes, and skeletons to and from the cloud Chunkflow: Running convolutional nets on large datasets DeepEM: Training convolutional nets to detect neuronal boundaries. DynamicAnnotationFramework: Proofreading and connectome updates (visit https://github. com/seung-lab/AnnotationPipelineOverview for repository list) Igneous: Coordinating downsampling, meshing, and data management. MeshParty: Interaction with meshes and mesh-based skeletonization (https://github. com/sdorkenw/MeshParty) MMAAPP: Watershed, size-dependent single linkage clustering, and mean affinity agglomeration. PyTorchUtils: Training convolutional nets for synapse detection and partner assignment (https://github.
f, OPC branch from P36 dataset contains a phagolysosome with vesicles (v) and an encapsulated axon (Ax). Scale bar, 300 nm. g, A portion of the same branch showing the phagolysosome (PL) and post synaptic density of a synapse onto an encapsulated axon (Ax) (arrows). Scale bar, 300 nm. h, 3D rendering of the same branch showing both the phagolysosome (PL) and the axon encapsulated collateral within the OPC cytoplasm. Scale bar, 500 nm. i-n, Examples of PLs from different OPCs showing the presence of 40 nm vesicles within the chambers. Scale bar, 300 nm. o, 3D reconstruction of an OPC from the P36 dataset. Yellow spheres represent manual annotations of the 189 phagolysosomes found in this OPC. Scale bar, 20 μm (original data in http://www. micronsexplorer. org/phagolysosomes/opc). p, 3D reconstruction of a microglial cell from the P36 dataset.
Scale bar, 750 nm. f, g, Plot shows the comparison of phagolysosome numbers in the microglia in P36 and P49 datasets. Extended data Figure 3. Premyelinating cells, myelinating oligodendrocyte and phagolysosome densities. a, 3D rendering of a premyelinating oligodendrocyte (OL) in P49 dataset shows a branch aligned with an axon (white)(boxed area). Scale bar, 20 µm. b, Higher magnification view of boxed area shows myelin (orange) and axon (white). Scale bar, 1. 5 µm. c, Ultrathin section view shows the uncompacted myelin(orange)(arrows) on the outside of the compact myelin (black). Scale bar, 750 nm. d, Mature myelinating oligodendrocyte (OL) has thin branches that are aligned with colored myelinated axons. Scale bar, 10µm. e-g, Plots comparing densities of phagolysosome distribution in OPCs, Premyelinating cells and mature oligodendrocytes.
Extended Data Figure 4. Transcriptomic and epigenomic profiling of neuronal subclass genes in OPCs. Dot plot of OPC to oligodendrocyte maturation genes (green) and neuronal subclass marker genes for all non-neuronal clusters and neuronal subclasses in dataset. Expression is scaled by gene across all clusters and subclasses. Blue indicates GABAergic and red indicates glutamatergic neuronal subclasses. Clusters with of gene body DNA hypomethylation are highlighted in yellow. Extended Figure 5. OPCs ingest collateral branches. a, OPC (pink) contacts an excitatory axon (gray). Boxed area shows site of ingestion. Scale bar, 5 μm. b, 3 D rendering of the excitatory axon (gray). Arrow points to collateral branch in boxed area in (a). Scale bar, 5 μm. c, Higher magnification of collateral branch. White arrow points to ingested branch in (d).
Our database was composed of 25 3D US volumes collected on 21 preterm nenonates at the age of 35. 8 ± 1. 6 gestational weeks. We found that the CPPN enables to encode CVS location, which increases the accuracy of the CNNs when they have few layers. Accuracy of the 2D and 3D FCNs reached intraobserver variability (IOV) in the case of dilated ventricles with Dice of 0. 893 ± 0. 008 and 0. 886 ± 0. 004 respectively (IOV = 0. 898 ± 0. 008) and with volume errors of 0. 45 ± 0. 42 cm 3 and 0. 36 ± 0. 24 cm 3 respectively (IOV = 0. 41 ± 0. 05 cm 3 ). 3D FCNs were more accurate than 2D FCNs in the case of normal ventricles with Dice of 0. 797 ± 0. 041 against 0. 776 ± 0. 038 (IOV = 0. 816 ± 0. 009) and volume errors of 0. 35 ± 0. 29 cm 3 against 0. 35 ± 0. 24 cm 3 (IOV = 0. 2 ± 0. 11 cm 3 ). The best segmentation time of volumes of size 320 × 320 × 320 was obtained by a 2D FCN in 3.
Automatic segmentation methods were proposed by  [24] ,  [25]  and  [26]. [24]  used a multi-altas registration algorithm to initialize segmentation which was then performed using a deformable mesh model. The authors reported Dice and MAD in  [24]  on their own dataset containing 12 infants whose age ranged from 2 to 8. 5 months. Respective values of 0. 708 ± 0. 036 and 0. 88 ± 0. 2 mm were obtained. The segmentation time was not reported. [25]  also initialize CVS segmentation utilizing a multi-atlas registration and performed segmentation with a multi-phase geodesic level-set method based on phase congruency maps. Their personal database contained 14 patients with IVH grade I to III whose gestational age ranged from 37 to 42 weeks. The authors reported a Dice, a MAD and a segmentation time of 0.
767 ± 0. 062, 1. 0 ± 0. 3 mm and 54 min per volume, respectively. Finally,  [26]  achieved CVS segmentation with a 2D U-net on a subset of the US database (15 patients) used in this article. The latter has the advantage of being very fast (only a few seconds compared to 54 minutes). The 3D US data segmentation algorithms described in this article are based on CNNs. The later have recently outperformed the state-of-the-art methods dedicated to image segmentation  [27, 28]. In particular, the use of CNNs for medical image semantic segmentation enables to reach outstanding levels of accuracy  [29, 30]  in a clinical time. Deep learning methods enable to learn relevant features directly from data. It is interesting in difficult problems, such as CVS segmentation, where the anatomical structure has a complex shape (Fig.
Contributions In this paper, we investigate the potential of 2D and 3D CNNs for CVS segmentation in 3D US data. We evaluate the benefits of using 2D FCN instead of 2D CNN which achieve pixelto-pixel segmentation. In addition, we compared 2D and 3D FCNs. Finally, we propose to use Compositional Pattern Producing Network (CPPN)  [36]  to enable the FCNs to learn CVS location. CPPNs are specific types of networks outputting shapes from input such as coordinates. Combined with CNNs low-level features, they can provide information about the features' location within the head. This approach has the advantage to learn the CVS-location directly from our dataset and enable the networks to be end-to-end trained. The accuracy of the networks was evaluated using two usual metrics (Dice and Mean Absolute Distance (MAD) and two medical metrics (absolute and relative volume difference).
US data description 2. 1. 1. Data acquisition For this study, 2D transfontanellar sonography acquisitions were performed on 21 preterm neonates at the neonatology center of the hospital of Avignon in France. These acquisitions corresponded to angular manual sweeps (Fig. 1. b) performed through the anterior fontanel with an Acuson Siemens 9L4 multi-D matrix transducer. A total of 25 acquisitions were obtained, each of them contained in average 249 ± 69 images of size 567 × 763, their spatial resolution was 0. 15 mm/pixel. The mean age of the infants at acquisition and at birth were respectively 35. 8 ± 1. 6 and 31. 9 ± 2. 9 gestational weeks.
Data pre-processing 3D-reconstructed US volumes were obtained from the 2D acquisitions with the reconstruction algorithm we described in  [26]. After 3D reconstruction (Fig. 2. c), the data was manually centered on the corpus callosum splenium (green points in Fig. 2. a. b) and rotated to the standard MRI coordinate system with axes x, y and z (Fig. 1. a). Then, the volumes were scaled to the half of their original size using bicubic interpolation and cropped to 320 × 320 × 320 voxels. This permitted defining a common coordinate system for all volumes and then computing identically normalized coordinate maps used as input for the CPPN. Finally, each image (respectively volume) were standardized to constitute the database used for 2D networks (respectively 3D networhs).
CVS description and data annotation CVS segmentation is part of the difficult segmentation problems: it deals with complex spatial structures with variable aspects in terms of image contrast. Fig. 2. a shows that the CVS is composed of a hyperechoic part and a hypoechoic part. The last one must not be confused with the Cavum Septi Pellucidi (CSP) visible in Fig. 2. b which is also hypoechoic. The complex anatomy of the CVS, which includes very thin parts, is described in Fig. 3. Manual CVS segmentations were performed on the US data acquisitions by a physician who checked all the reconstructed   annotated volumes (Fig. 2. d) for anatomical and spatial consistency. These reference segmentations were used to measure each CVS volume, its mean value and the number of normal and dilated CVS composing the dataset are given in the last row of Table.
MRI data description To make our results comparable, we sought an open-access database that contains 3D manual annotations of the premature child's CVS. Unfortunately, to our knowledge, there is no US database that meets these criteria. The only MRI database meeting these criteria (  [37] ) contained few available annotated volumes. We therefore used an MRI database, from the OASIS project (  [38] ) containing a reasonable number of volumes with manual annotations of the CVS of the young adult.
Data description We used data from the MICCAI challenge on multi-atlas labelling  [39] , this database contains 35 annotated volumes divided into 15 training volumes and 20 test volumes. From the 15 training volumes, we used 4 volumes for validation. These images were acquired with a Siemens Vision 1. 5T MRI in the sagittal plan at a resolution of 1 × 1 × 1. 25 mm per voxel and were resized to a resolution of 1 × 1 × 1 mm. The images were originaly segmented into 134 classes in coronal plan. For the need of our study, we only retained and fusioned the classes corresponding to CVS.
In the case of the MRI database, the size of the input patches was 75 × 75, 50 × 50 and 25 × 25 as defined in  [17]. In the case of the US dataset, as the pixel resolution was smaller, we used sizes of 127 × 127, 101 × 101 and 75 × 75 and consequently adapted the number of neurons in the fully connected layers. The desired output was the class of the pixel in the center of the patches which was represented by 1 for VL and 0 for background. The convolution sizes defined in  [17]  were used. As for U-net and Vnet, we used batch normalization and the Softmax function right after the classification layer but no dropout was applied.