intern: Integrated Toolkit for Extensible and Reproducible Neuroscience

Matelsky, Jordan; Rodríguez, Luis M.; Xenes, Daniel; Gion, Timothy; Hider, Robert; Wester, Brock A.; Gray-Roncal, William

doi:10.1101/2020.05.15.098707

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…SABER provides tools to connect to specialized neuroimaging databases that integrate into CWL tool pipelines. We use intern [ 57 , 58 ] to provide access to bossDB and DVID and abstract data storage, RESTful calls, and access details. Workflow parameters, objective functions, and summary results such as graphs and cell densities can be stored using a DataJoint database [ 21 ] using a custom set of table schemas.…”

Section: Methodsmentioning

confidence: 99%

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

et al. 2020

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Background Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. Results We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Conclusions Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains.

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Section: Methodsmentioning

confidence: 99%

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

et al. 2020

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“…For benchmarking, data are accessed through the Python intern API (43). This API allows a remote connection to the BossDB system, including downloads of arbitrary, on-demand 3D cutouts of data, including raw images and annotations, without the need to download the entire dataset to disk.…”

Section: Data Accessmentioning

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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“…We would like to thank Cora Mentor Roy and Kevin Romero for their help with usability testing and community resource development. This manuscript has been released as a pre-print at https://doi.org/10.1101/2020.05.15.098707 [ 26 ].…”

Section: Acknowledgmentmentioning

confidence: 99%

An Integrated Toolkit for Extensible and Reproducible Neuroscience

Matelsky

Rodríguez

Xenes

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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As neuroimagery datasets continue to grow in size, the complexity of data analyses can require a detailed understanding and implementation of systems computer science for storage, access, processing, and sharing. Currently, several general data standards (e.g., Zarr, HDF5, precomputed) and purpose-built ecosystems (e.g., BossDB, CloudVolume, DVID, and Knossos) exist. Each of these systems has advantages and limitations and is most appropriate for different use cases. Using datasets that don't fit into RAM in this heterogeneous environment is challenging, and significant barriers exist to leverage underlying research investments. In this manuscript, we outline our perspective for how to approach this challenge through the use of community provided, standardized interfaces that unify various computational backends and abstract computer science challenges from the scientist. We introduce desirable design patterns and share our reference implementation called intern.

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intern: Integrated Toolkit for Extensible and Reproducible Neuroscience

Cited by 3 publications

References 14 publications

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

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

An Integrated Toolkit for Extensible and Reproducible Neuroscience

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