{"url":"/dataset/cremi","name":"CREMI","full_name":null,"description_markdown":"MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images.\r\n# About\r\nThe goal of this challenge is to evaluate algorithms for automatic reconstruction of neurons and neuronal connectivity from serial section electron microscopy data. The comparison is performed not only by evaluating the quality of neuron segmentations, but also by assessing the accuracy of detecting synapses and identifying synaptic partners. The challenge is carried out on three large and diverse datasets from adult Drosophila melanogaster brain tissue, comprising neuron segmentation ground truth and annotations for synaptic connections. A successful solution would demonstrate its efficiency and generalizability, and carry great potential to reduce the time spent on manual reconstruction of neural circuits in electron microscopy volumes.\r\n\r\n# Description\r\nWe provide three datasets, each consisting of two (5 μm)3 volumes (training and testing, each 1250 px × 1250 px × 125 px) of serial section EM of the adult fly brain. Each volume has neuron and synapse labelings and annotations for pre- and post-synaptic partners.","description_withheld":null,"homepage":"https://cremi.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Biomedical","url":"/datasets/modality/biomedical"}],"tasks":[{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","datasets_with_task":"/datasets/task/3d-instance-segmentation-1"},{"name":"Brain Image Segmentation","url":"/task/brain-image-segmentation","datasets_with_task":"/datasets/task/brain-image-segmentation"}],"languages":[],"variants":["CREMI"],"data_loaders":[{"repo":"https://github.com/cremi/cremi_python","url":"https://github.com/cremi/cremi_python","frameworks":[]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/brain-image-segmentation-on-cremi","task":"Brain Image Segmentation","dataset_variant":"CREMI","rows":1,"metrics":["CREMI Score","VOI"],"first_row_in_archive_order":{"model":"U-NET MALA","paper":"/paper/a-deep-structured-learning-approach-towards","metrics":{"CREMI Score":"0.289","VOI":"0.606"},"code_links":[{"title":"funkey/mala","url":"https://github.com/funkey/mala"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-deep-structured-learning-approach-towards","title":"Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction","date":"2017-09-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}