@article {69567, title = {Decomposing heterogeneous dynamical systems with graph neural networks}, journal = {arXiv}, year = {2024}, month = {2024 Jul 27}, abstract = {

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

}, doi = {10.48550/arXiv.2407.19160}, url = {https://doi.org/10.48550/arXiv.2407.19160}, author = {Allier, C{\'e}dric and Schneider, Magdalena C and Innerberger, Michael and Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {68714, title = {The big warp: Registration of disparate retinal imaging modalities and an example overlay of ultrawide-field photos and en-face OCTA images.}, journal = {PLoS One}, volume = {18}, year = {2023}, month = {2023 Apr 25}, pages = {e0284905}, abstract = {

PURPOSE: To develop an algorithm and scripts to combine disparate multimodal imaging modalities and show its use by overlaying en-face optical coherence tomography angiography (OCTA) images and Optos ultra-widefield (UWF) retinal images using the Fiji (ImageJ) plugin BigWarp.

METHODS: Optos UWF images and Heidelberg en-face OCTA images were collected from various patients as part of their routine care. En-face OCTA images were generated and ten (10) images at varying retinal depths were exported. The Fiji plugin BigWarp was used to transform the Optos UWF image onto the en-face OCTA image using matching reference points in the retinal vasculature surrounding the macula. The images were then overlayed and stacked to create a series of ten combined Optos UWF and en-face OCTA images of increasing retinal depths. The first algorithm was modified to include two scripts that automatically aligned all the en-face OCTA images.

RESULTS: The Optos UWF image could easily be transformed to the en-face OCTA images using BigWarp with common vessel branch point landmarks in the vasculature. The resulting warped Optos image was then successfully superimposed onto the ten Optos UWF images. The scripts more easily allowed for automatic overlay of the images.

CONCLUSIONS: Optos UWF images can be successfully superimposed onto en-face OCTA images using freely available software that has been applied to ocular use. This synthesis of multimodal imaging may increase their potential diagnostic value. Script A is publicly available at https://doi.org/10.6084/m9.figshare.16879591.v1 and Script B is available at https://doi.org/10.6084/m9.figshare.17330048.

}, keywords = {Fluorescein Angiography, Fundus Oculi, Humans, Retina, Retinal Vessels, Tomography, Optical Coherence}, issn = {1932-6203}, doi = {10.1371/journal.pone.0284905}, author = {Thuma, Tobin B T and Bogovic, John A and Gunton, Kammi B and Jimenez, Hiram and Negreiros, Bernardo and Pulido, Jose S} } @article {68897, title = {Toward scalable reuse of vEM data: OME-Zarr to the rescue.}, journal = {Methods in Cell Biology}, volume = {177}, year = {2023}, month = {2023 Jul 20}, pages = {359-387}, abstract = {

The growing size of EM volumes is a significant barrier to findable, accessible, interoperable, and reusable (FAIR) sharing. Storage, sharing, visualization and processing are challenging for large datasets. Here we discuss a recent development toward the standardized storage of volume electron microscopy (vEM) data which addresses many of the issues that researchers face. The OME-Zarr format splits data into more manageable, performant chunks enabling streaming-based access, and unifies important metadata such as multiresolution pyramid descriptions. The file format is designed for centralized and remote storage (e.g., cloud storage or file system) and is therefore ideal for sharing large data. By coalescing on a common, community-wide format, these benefits will expand as ever more data is made available to the scientific community.

}, keywords = {Information Storage and Retrieval, Volume Electron Microscopy}, issn = {0091-679X}, doi = {10.1016/bs.mcb.2023.01.016}, author = {Rzepka, Norman and Bogovic, John A and Moore, Joshua A} } @article {68958, title = {Towards Generalizable Organelle Segmentation in Volume Electron Microscopy.}, journal = {Microscopy and Microanalysis}, volume = {29}, year = {2023}, month = {2023 Jul 22}, pages = {975}, keywords = {Organelles, Volume Electron Microscopy}, issn = {1435-8115}, doi = {10.1093/micmic/ozad067.487}, author = {Heinrich, Larissa and Patton, Will and Bennett, Davis and Ackerman, David and Park, Grace and Bogovic, John A and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Shan Xu, C and Funke, Jan and Korff, Wyatt and Hess, Harald and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey} } @article {65281, title = {Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells.}, journal = {Science}, volume = {367}, year = {2020}, month = {2020 Jan 17}, abstract = {

Within cells, the spatial compartmentalization of thousands of distinct proteins serves a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) can elucidate protein spatial relationships to global ultrastructure, but has suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional cryogenic SR and focused ion beam-milled block-face EM across entire vitreously frozen cells. The approach preserves ultrastructure while enabling independent SR and EM workflow optimization. We discovered unexpected protein-ultrastructure relationships in mammalian cells including intranuclear vesicles containing endoplasmic reticulum-associated proteins, web-like adhesions between cultured neurons, and chromatin domains subclassified on the basis of transcriptional activity. Our findings illustrate the value of a comprehensive multimodal view of ultrastructural variability across whole cells.

}, issn = {1095-9203}, doi = {10.1126/science.aaz5357}, author = {Hoffman, David P and Shtengel, Gleb and Xu, C Shan and Campbell, Kirby R and Freeman, Melanie and Wang, Lei and Milkie, Daniel E and Pasolli, H Amalia and Iyer, Nirmala and Bogovic, John A and Stabley, Daniel R and Shirinifard, Abbas and Pang, Song and Peale, David and Schaefer, Kathy and Pomp, Wim and Chang, Chi-Lun and Lippincott-Schwartz, Jennifer and Kirchhausen, Tom and Solecki, David J and Betzig, Eric and Hess, Harald F} } @article {65910, title = {An unbiased template of the Drosophila brain and ventral nerve cord.}, journal = {PLoS One}, volume = {15}, year = {2020}, month = {2020 Dec 31}, pages = {e0236495}, abstract = {

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0236495}, author = {Bogovic, John A and Otsuna, Hideo and Heinrich, Larissa and Ito, Masayoshi and Jeter, Jennifer and Meissner, Geoffrey and Nern, Aljoscha and Colonell, Jennifer and Malkesman, Oz and Ito, Kei and Saalfeld, Stephan} } @article {51795, title = {Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution.}, journal = {Science (New York, N.Y.)}, volume = {363}, year = {2019}, month = {2019 Jan 18}, pages = {eaau8302}, abstract = {

Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire\ Drosophila\ brain. These included synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.

}, doi = {10.1126/science.aau8302}, author = {Gao, Ruixuan and Asano, Shoh M and Upadhyayula, Srigokul and Pisarev, Igor and Milkie, Daniel E and Liu, Tsung-Li and Singh, Ved and Graves, Austin R and Huynh, Grace H and Zhao, Yongxin and Bogovic, John A and Colonell, Jennifer and Ott, Carolyn M and Zugates, Christopher T and Tappan, Susan and Rodriguez, Alfredo and Mosaliganti, Kishore R and Sheu, Shu-Hsien and Pasolli, H Amalia and et al} } @article {48620, title = {A complete electron microscopy volume of the brain of adult Drosophila melanogaster.}, journal = {Cell}, volume = {174}, year = {2018}, month = {2018 Jul 12}, pages = {730-43}, abstract = {

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience..

}, doi = {10.1016/j.cell.2018.06.019}, author = {Zheng, Zhihao and Lauritzen, J Scott and Perlman, Eric and Robinson, Camenzind G and Nichols, Matthew and Milkie, Daniel E and Torrens, Omar and Price, John and Fisher, Corey B and Sharifi, Nadiya and Calle-Schuler, Steven A and Kmecova, Lucia and Ali, Iqbal J and Karsh, Bill and Trautman, Eric T and Bogovic, John A and Hanslovsky, Philipp and Jefferis, Gregory S X E and Kazhdan, Michael and Khairy, Khaled} } @article {49386, title = {An unbiased template of the Drosophila brain and ventral nerve cord.}, journal = {bioRxiv}, year = {2018}, month = {2018 Jul 25}, abstract = {

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

}, doi = {10.1101/376384}, author = {Bogovic, John A and Otsuna, Hideo and Heinrich, Larissa and Ito, Masayoshi and Jeter, Jennifer and Meissner, Geoffrey W and Nern, Aljoscha and Colonell, Jennifer and Malkesman, Oz and Saalfeld, Stephan} } @article {48602, title = {Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.}, journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science}, volume = {10434}, year = {2017}, month = {2017 Sept 4}, pages = {arXiv:1706.03142}, abstract = {

The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings.

Our results indicate that both architectures can successfully generate 3D isotropic super-resolution from non-isotropic EM, with the U-Net performing consistently better. We propose several promising directions for practical application.

}, doi = {10.1007/978-3-319-66185-8_16}, author = {Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {48296, title = {Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.}, journal = {Bioinformatics (Oxford, England)}, year = {2016}, month = {2016 Dec 23}, abstract = {

MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw794}, author = {Hanslovsky, Philipp and Bogovic, John A and Saalfeld, Stephan} } @conference {47735, title = {Robust registration of calcium images by learned contrast synthesis.}, booktitle = {IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro}, year = {2016}, month = {2016 Jun 15}, address = {Clarion Congress Hotel in Prague Prague; Czech Republic}, abstract = {

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40\% to 15\%) by using a synthesized image.

}, isbn = {978-147992350-2}, doi = {10.1109/ISBI.2016.7493463}, author = {Bogovic, John A and Hanslovsky, Philipp and Wong, Allen M and Saalfeld, Stephan} } @article {47086, title = {Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.}, journal = {NeuroImage}, volume = {127}, year = {2015}, month = {2015 Sep 23}, pages = {435-44}, abstract = {

The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 2(j) 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes show distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2015.09.032}, author = {Yang, Zhen and Ye, Chuyang and Bogovic, John A and Carass, Aaron and Jedynak, Bruno M and Ying, Sarah H and Prince, Jerry L} } @article {46788, title = {Post-acquisition image based compensation for thickness variation in microscopy section series.}, journal = {IEEE International Symposium on Biomedical Imaging}, year = {2015}, month = {2014 Nov 25}, pages = {507-11}, abstract = {

Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.

}, url = {http://arxiv.org/abs/1411.6970}, author = {Hanslovsky, Philpp and Bogovic, John A and Saalfeld, Stephan} }