Probabilistic Joint Segmentation and Labeling of C. elegans Neurons

Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Oliver Hobert, Liam Paninski

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Automatic identification and segmentation of the neurons of C. elegans enables evaluating nervous system mutations, positional variability, and allows us to conduct high-throughput population studies employing many animals. A recently introduced transgene of C. elegans, named “NeuroPAL” has enabled the efficient annotation of neurons and the construction of a statistical atlas of their positions. Previous atlas-based segmentation approaches have modeled images of cells as a mixture model. The expectation-maximization (EM) algorithm and its variants are used to find the (local) maximum likelihood parameters for this class of models. We present a variation of the EM algorithm called Sinkhorn-EM (sEM) that uses regularized optimal transport Sinkhorn iterations to enforce constraints on the marginals of the joint distribution of observed variables and latent assignments in order to incorporate our prior information about cell sizes into the cluster-data assignment proportions. We apply our method to the problem of segmenting and labeling neurons in fluorescent microscopy images of C. elegans specimens. We show empirically that sEM outperforms vanilla EM and a recently proposed 3-step (filter, detect, identify) labeling approach. Open source code implementing this method is available at

    Original languageEnglish (US)
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
    EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
    PublisherSpringer Science and Business Media Deutschland GmbH
    Number of pages11
    ISBN (Print)9783030597214
    StatePublished - 2020
    Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
    Duration: Oct 4 2020Oct 8 2020

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12265 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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