Jeremy Maitin-Shepard
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Multi-Layered Maps of Neuropil with Segmentation Guided Contrastive Learning
Sven Dorkenwald
Daniel R. Berger
Agnes L. Bodor
Forrest Collman
Casey M. Schneider-Mizell
Nuno Maçarico da Costa
Jeff W. Lichtman
Nature Methods (2023)
Preview abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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A connectomic study of a petascale fragment of human cerebral cortex
Alex Shapson-Coe
Daniel R. Berger
Yuelong Wu
Richard L. Schalek
Shuohong Wang
Neha Karlupia
Sven Dorkenwald
Evelina Sjostedt
Dongil Lee
Luke Bailey
Angerica Fitzmaurice
Rohin Kar
Benjamin Field
Hank Wu
Julian Wagner-Carena
David Aley
Joanna Lau
Zudi Lin
Donglai Wei
Hanspeter Pfister
Adi Peleg
Jeff W. Lichtman
bioRxiv (2021)
Preview abstract
We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.
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A connectome and analysis of the adult Drosophila central brain
Louis K Scheffer
C Shan Xu
Zhiyuan Lu
Shin-ya Takemura
Kenneth J Hayworth
Gary B Huang
Kazunori Shinomiya
Stuart Berg
Jody Clements
Philip M Hubbard
William T Katz
Lowell Umayam
Ting Zhao
David Ackerman
John Bogovic
Tom Dolafi
Dagmar Kainmueller
Takashi Kawase
Khaled A Khairy
Larry Lindsey
Nicole Neubarth
Donald J Olbris
Hideo Otsuna
Eric T Trautman
Masayoshi Ito
Alexander S Bates
Jens Goldammer
Tanya Wolff
Robert Svirskas
Philipp Schlegel
Erika Neace
Christopher J Knecht
Chelsea X Alvarado
Dennis A Bailey
Samantha Ballinger
Jolanta A Borycz
Brandon S Canino
Natasha Cheatham
Michael Cook
Marisa Dreher
Octave Duclos
Bryon Eubanks
Kelli Fairbanks
Samantha Finley
Nora Forknall
Audrey Francis
Gary Patrick Hopkins
Emily M Joyce
SungJin Kim
Nicole A Kirk
Julie Kovalyak
Shirley Lauchie
Alanna Lohff
Charli Maldonado
Emily A Manley
Sari McLin
Caroline Mooney
Miatta Ndama
Omotara Ogundeyi
Nneoma Okeoma
Christopher Ordish
Nicholas Padilla
Christopher M Patrick
Tyler Paterson
Elliott E Phillips
Emily M Phillips
Neha Rampally
Caitlin Ribeiro
Madelaine K Robertson
Jon Thomson Rymer
Sean M Ryan
Megan Sammons
Anne K Scott
Ashley L Scott
Aya Shinomiya
Claire Smith
Kelsey Smith
Natalie L Smith
Margaret A Sobeski
Alia Suleiman
Jackie Swift
Satoko Takemura
Iris Talebi
Dorota Tarnogorska
Emily Tenshaw
Temour Tokhi
John J Walsh
Tansy Yang
Jane Anne Horne
Feng Li
Ruchi Parekh
Patricia K Rivlin
Vivek Jayaraman
Marta Costa
Gregory SXE Jefferis
Kei Ito
Stephan Saalfeld
Reed George
Ian Meinertzhagen
Gerald M Rubin
Harald F Hess
Stephen M Plaza
eLife, vol. 9 (2020)
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The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.
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Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment
Larry Lindsey
Zhihao Zheng
Alexander Shakeel Bates
István Taisz
Matthew Nichols
Feng Li
Eric Perlman
Gregory S.X.E. Jefferis
Davi Bock
bioRxiv (2019)
Preview abstract
Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.
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High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks
Jörgen Kornfeld
Larry Lindsey
Winfried Denk
Nature Methods (2018)
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Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.
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Combinatorial Energy Learning for Image Segmentation
Pieter Abbeel
Advances in Neural Information Processing Systems 29 (2016)
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We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.
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Flood-Filling Networks
Jörgen Kornfeld
Winfried Denk
CoRR, vol. abs/1611.00421 (2016)
Preview abstract
State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments. Prior work has varied the complexity and approach employed in these two steps, including the incorporation of multi-layer neural networks to perform boundary prediction, and the use of global optimizations during pixel clustering. We propose a unified and end-to-end trainable machine learning approach, flood-filling networks, in which a recurrent 3d convolutional network directly produces individual segments from a raw image. The proposed approach robustly segments images with an unknown and variable number of objects as well as highly variable object sizes. We demonstrate the approach on a challenging 3d image segmentation task, connectomic reconstruction from volume electron microscopy data, on which flood-filling neural networks substantially improve accuracy over other state-of-the-art methods. The proposed approach can replace complex multi-step segmentation pipelines with a single neural network that is learned end-to-end.
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