@article {69568, title = {DaCapo: a modular deep learning framework for scalable 3D image segmentation}, journal = {arXiv}, year = {2024}, month = {2024 Aug 05}, abstract = {

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo{\textquoteright}s unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

}, doi = {10.48550/arXiv.2408.02834}, url = {https://doi.org/10.48550/arXiv.2408.02834}, author = {Patton, William and Jeff L Rhoades and Zouinkhi, Marwan and Ackerman, David G and Malin-Mayor, Caroline and Adjavon, Diane and Heinrich, Larissa and Bennett, Davis and Zubov, Yurii and Team, CellMap Project and Weigel, Aubrey and Funke, Jan} } @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 {68807, title = {Architecture and dynamics of a desmosome-endoplasmic reticulum complex.}, journal = {Nature Cell Biology}, volume = {25}, year = {2023}, month = {2023 Jun 16}, pages = {823-835}, abstract = {

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.

}, keywords = {Cytoskeleton, Desmosomes, Endoplasmic Reticulum, Intermediate Filaments, Keratins}, issn = {1476-4679}, doi = {10.1038/s41556-023-01154-4}, author = {Bharathan, Navaneetha Krishnan and Giang, William and Hoffman, Coryn L and Aaron, Jesse S and Khuon, Satya and Chew, Teng-Leong and Preibisch, Stephan and Trautman, Eric T and Heinrich, Larissa and Bogovic, John and Bennett, Davis and Ackerman, David and Park, Woohyun and Petruncio, Alyson and Weigel, Aubrey V and Saalfeld, Stephan and Wayne Vogl, A and Stahley, Sara N and Kowalczyk, Andrew P} } @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 {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {67750, title = {Whole-cell organelle segmentation in volume electron microscopy.}, journal = {Nature}, volume = {599}, year = {2021}, month = {2021 Nov 01}, pages = {141-46}, abstract = {

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, {\textquoteright}OpenOrganelle{\textquoteright}, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03977-3}, author = {Heinrich, Larissa and Bennett, Davis and Ackerman, David and Park, Woohyun and Bogovic, John and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Xu, C Shan and Funke, Jan and Korff, Wyatt and Hess, Harald F and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey V} } @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 {49220, title = {Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain.}, journal = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2018}, year = {2018}, month = {2018 Sep 26}, pages = {317-25}, abstract = {

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections.\ 
Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation.\ 
We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art.\ 
We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

}, doi = {10.1007/978-3-030-00934-2_36}, author = {Heinrich, Larissa and Funke, Jan and Pape, Constantin and Nunez-Iglesias, Juan and Saalfeld, Stephan} } @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} }