AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

Wei, Donglai; Lee, Ki-Suk; Li, Hanyu; Lu, Ran; Bae, Jinsoo; Liu, Zequan; Zhang, Lifu; Santos, Márcia dos; Lin, Zudi; Uram, Thomas D.; Wang, Xueying; Arganda‐Carreras, Ignacio; Matejek, Brian; Kasthuri, Narayanan; Lichtman, Jeff W.; Pfister, Hanspeter

doi:10.1007/978-3-030-87193-2_17

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…Other problem formulations include 2D image segmentation of cell membranes (ISBI 2012 Challenge), with data from (33), and 3D segmentation of cells (34; 35). Specific benchmarks have also been developed for axon instance segmentation (36) and mitochondria segmentation (37). Different forms of microscopy with micrometer resolution, including calcium fluorescence microscopy, are suitable for segmenting cell bodies and extracting functional time-series data, but lack other microstructure information.…”

Section: Existing Datasets and Benchmarks For Resolving Brain Structurementioning

confidence: 99%

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

Quesada¹,

Sathidevi²,

Liu³

et al. 2023

Preprint

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There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and selfsupervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

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Section: Existing Datasets and Benchmarks For Resolving Brain Structurementioning

confidence: 99%

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

Quesada¹,

Sathidevi²,

Liu³

et al. 2023

Preprint

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“…In 2015, Kasthuri et al [KHB * 15] collected 660 GB of EM images of a mouse's neocortex with partial segmentation. Wei and Lin [WLFB * 20,WLL * 21,LWP * 21] made proofread segmentation volumes for axons, mitochondria and cell nuclei publicly available.…”

Section: Public Data Sets and Open Source Toolsmentioning

confidence: 99%

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Beyer

Troidl

Boorboor³

et al. 2022

Computer Graphics Forum

Self Cite

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The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

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“…And in [10], a high-throughput deep learning pipeline is proposed to tackle the more challenging problem of unmyelinated axon instance segmentation. There also exists a public dataset called AxonEM [27], which is a large-scale EM dataset from the human and mouse cortex that provides dense ground truth instance segmentation of axons.…”

Section: Introductionmentioning

confidence: 99%

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

Nguyen¹,

Narwani²,

Larson³

et al. 2023

Preprint

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The wiring and connectivity of neurons form a critical structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking.Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here.In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. The participants will have the flexibility to use either or both of the provided annotations to train their models, and are challenged to submit an accurate voxel-wise prediction on the test volume. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.

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AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

Cited by 10 publications

References 32 publications

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

A Survey of Visualization and Analysis in High‐Resolution Connectomics

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

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