Soma Detection in 3D Images of Neurons using Machine Learning Technique

Guan Wei He, Ting Yuan Wang, Ann Shyn Chiang, Yu-Tai Ching*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.

Original languageEnglish
Pages (from-to)31-41
Number of pages11
Issue number1
StatePublished - 1 Jan 2018


  • Drosophila
  • Machine learning method
  • Soma detection

Fingerprint Dive into the research topics of 'Soma Detection in 3D Images of Neurons using Machine Learning Technique'. Together they form a unique fingerprint.

Cite this