Learning to Correct Sloppy Annotations in Electron Microscopy Volumes

Minghao Chen*, Mukesh Bangalore Renuka, Lu Mi, Jeff Lichtman, Nir Shavit, Yaron Meirovitch

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Connectomics deals with the problem of reconstructing neural circuitry from electron microscopy images at the synaptic level. Automatically reconstructing circuits from these volumes requires high fidelity 3-D instance segmentation, which yet appears to be a daunting task for current computer vision algorithms. Hence, to date, most datasets are not reconstructed by fully-automated methods. Even after painstaking proofreading, these methods still produce numerous small errors. In this paper, we propose an approach to accelerate manual reconstructions by learning to correct imperfect manual annotations. To achieve this, we designed a novel solution for the canonical problem of marker-based 2-D instance segmentation, reporting a new state-of-the-art for region-growing algorithms demonstrated on challenging electron microscopy image stacks. We use our marker-based instance segmentation algorithm to learn to correct all "sloppy"object annotations by reducing and expanding all annotations. Our correction algorithm results in high quality morphological reconstruction (near ground truth quality), while significantly cutting annotation time (~8x) for several examples in connectomics. We demonstrate the accuracy of our approach on public connectomics benchmarks and on a set of large-scale neuron reconstruction problems, including on a new octopus dataset that cannot be automatically segmented at scale by existing algorithms.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
PublisherIEEE Computer Society
Pages4273-4284
Number of pages12
ISBN (Electronic)9798350302493
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2023-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Country/TerritoryCanada
CityVancouver
Period18/06/2322/06/23

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