3D Neuron Reconstruction in Tangled Neuronal Image with Deep Networks

Qiufu Li, Linlin Shen

Research output: Journal PublicationArticlepeer-review

22 Citations (Scopus)

Abstract

Digital reconstruction or tracing of 3D neuron is essential for understanding the brain functions. While existing automatic tracing algorithms work well for the clean neuronal image with a single neuron, they are not robust to trace the neuron surrounded by nerve fibers. We propose a 3D U-Net-based network, namely 3D U-Net Plus, to segment the neuron from the surrounding fibers before the application of tracing algorithms. All the images in BigNeuron, the biggest available neuronal image dataset, contain clean neurons with no interference of nerve fibers, which are not practical to train the segmentation network. Based upon the BigNeuron images, we synthesize a SYNethic TAngled NEuronal Image dataset (SYNTANEI) to train the proposed network, by fusing the neurons with extracted nerve fibers. Due to the adoption of dropout, àtrous convolution and Àtrous Spatial Pyramid Pooling (ASPP), experimental results on the synthetic and real tangled neuronal images show that the proposed 3D U-Net Plus network achieved very promising segmentation results. The neurons reconstructed by the tracing algorithm using the segmentation result match significantly better with the ground truth than that using the original images.

Original languageEnglish
Article number8758392
Pages (from-to)425-435
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number2
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Keywords

  • 3D U-Net Plus
  • image segmentation
  • Neuron reconstruction
  • SYNTANEI

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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