mlsanityData to 7 Oct 2026

Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images

arXiv 2101.08910 ↗First posted 22 Jan 2021eess.IV cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation. However, they are always hard to segment due to the lack in quality. Despite a series of attempts to apply convolutional neural networks (CNNs) on this task, noise and disconnected gaps are still challenging to alleviate with the neglect of the non-local features of graph-like tubular neural structures. Hence, we present an end-to-end segmentation network by jointly considering the local appearance and the global geometry traits through graph reasoning and a skeleton-based auxiliary loss. The evaluation results on the Janelia dataset from the BigNeuron project demonstrate that our proposed method exceeds the counterpart algorithms in performance.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

13 of 22 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  8. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  9. 2018Graph-Based Global Reasoning Networkscited by 120
  10. 2019Graphonomy: Universal Human Parsing via Graph Transfer Learningcited by 81
  11. 2018Deep Vessel Segmentation By Learning Graphical Connectivitycited by 43
  12. 2019A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTscited by 27
1 more
  1. 2020GINet: Graph Interaction Network for Scene Parsingcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2022Local Intensity Order Transformation for Robust Curvilinear Object Segmentationcited by 12
  2. 2022PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Cloudscited by 6
  3. 2021Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstructioncited by 4
  4. 2024Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Imagescited by 2
  5. 2024DINeuro: Distilling Knowledge from 2D Natural Images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstructioncited by 1
  6. 2025TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracycited by 1
  7. 2026NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentationcited by 0