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TOPOLOGY-PRESERVING DEEP SUPERVISION FOR 3D AXON CENTERLINE SEGMENTATION USING PARTIALLY ANNOTATED DATA.

Roshan Kenia1, Fin Amin1, Benjamin W Roop1

  • 1MIT Lincoln Laboratory.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 9, 2025
PubMed
Summary

This study introduces a new method for brain connectivity analysis, improving axon centerline detection with less annotated data. Our approach achieves high accuracy using only 50% of annotations, significantly reducing manual effort.

Keywords:
3D segmentationaxon tracingdeep supervisiontopology-preservation

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Biomedical Imaging

Background:

  • Accurate axon centerline detection is crucial for mapping brain connectivity.
  • Manual annotation of 3D brain imagery is labor-intensive and expensive.
  • Developing methods that utilize limited or incomplete annotations is essential.

Purpose of the Study:

  • To develop an accurate axon centerline detection technique that requires less annotated data.
  • To address the challenge of incomplete annotations in 3D brain imagery.
  • To improve the efficiency of brain connectivity analysis.

Main Methods:

  • A novel topology-preserving loss function was developed.
  • A deep supervision paradigm was integrated into the training process.
  • The method was trained and evaluated using 3D brain volumes with varying levels of expert annotations.

Main Results:

  • The proposed training paradigm significantly outperforms existing methods.
  • Comparable performance was achieved using only 50% of the required annotations.
  • The baseline method required 75% of annotations to achieve similar results.

Conclusions:

  • The developed method effectively reduces the need for extensive manual annotation in brain imaging.
  • This approach accelerates the process of understanding brain connectivity and functionality.
  • The topology-preserving loss and deep supervision enhance accuracy with limited data.