Segmenting neuronal structure in 3D optical microscope images via knowledge distillation with teacher-student network

Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Yue Wang, Dagan Feng, Hanchuan Peng, Weidong Cai

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

27 Citations (Scopus)


Three-dimensional (3D) volumetric neural image segmentation is crucial to reconstructing accurate neuron structures. However, due to the structural complexity of neurons and the diverse imaging qualities of the microscopes, it is challenging to achieve both accuracy and efficiency. In this paper, we propose a teacher-student learning framework for fast neuron segmentation. The segmentation inference is performed using a light-weighted student network which benefits from knowledge distillation of a teacher network with a higher capacity. Evaluated on the Janelia dataset from the BigNeuron project, our proposed framework achieves competitive performance for segmentation accuracy while reducing the computational cost to facilitate large-scale processing.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE International Symposium on Biomedical Imaging
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538636411
ISBN (Print)9781538636404
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2019 - Hilton Molino Stucky, Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16th (Proceedings)

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2019
Abbreviated titleISBI 2019
Internet address


  • Bigneuron
  • Knowledge distillation
  • Neuronal image segmentation
  • Teacher-student network

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