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Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data

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arxiv 2011.03204 v1 pith:CERCRLDL submitted 2020-11-06 cs.DC eess.IV

classification cs.DCeess.IV
keywords pipelinecodesdataelectronindividuallargemodularprocessing
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We present a fully modular and scalable software pipeline for processing electron microscope (EM) images of brain slices into 3D visualization of individual neurons and demonstrate an end-to-end segmentation of a large EM volume using a supercomputer. Our pipeline scales multiple packages used by the EM community with minimal changes to the original source codes. We tested each step of the pipeline individually, on a workstation, a cluster, and a supercomputer. Furthermore, we can compose workflows from these operations using a Balsam database that can be triggered during the data acquisition or with the use of different front ends and control the granularity of the pipeline execution. We describe the implementation of our pipeline and modifications required to integrate and scale up existing codes. The modular nature of our environment enables diverse research groups to contribute to the pipeline without disrupting the workflow, i.e. new individual codes can be easily integrated for each step on the pipeline.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Removing constraints of 4D-STEM with a framework for event-driven acquisition and processing

    physics.ins-det 2025-05 conditional novelty 6.0 of 10

    Event-driven 4D-STEM, processed without ever forming dense frames, runs live on consumer hardware and can shrink datasets by orders of magnitude in low-dose and large-scan regimes.

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