• Corpus ID: 211021047

Efficient 2D neuron boundary segmentation with local topological constraints

@article{Ambegoda2020Efficient2N,
  title={Efficient 2D neuron boundary segmentation with local topological constraints},
  author={Thanuja D. Ambegoda and Matthew Cook},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.01036}
}
We present a method for segmenting neuron membranes in 2D electron microscopy imagery. This segmentation task has been a bottleneck to reconstruction efforts of the brain's synaptic circuits. One common problem is the misclassification of blurry membrane fragments as cell interior, which leads to merging of two adjacent neuron sections into one via the blurry membrane region. Human annotators can easily avoid such errors by implicitly performing gap completion, taking into account the… 

Figures from this paper

References

SHOWING 1-10 OF 21 REFERENCES
Efficient automatic 3D-reconstruction of branching neurons from EM data
TLDR
The core of the system is a set of possible assignments, each of which proposes with some cost a link between neuron regions in consecutive sections, which can model the continuation, branching, and end of neurons.
Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images
Segmentation fusion for connectomics
TLDR
This work identifies the fusion of segments and links that provide the most globally consistent segmentation of the stack and shows that this two-step approach of pre-enumeration and posterior fusion yields significant advantages and provides state-of-the-art reconstruction results.
Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes
TLDR
This paper proposes to replace the heuristic set of candidates used in previous methods with samples drawn from a conditional random field (CRF) that is trained to label sections of neural tissue that produce 30% less reconstruction errors than current candidate generation methods.
Globally Optimal Closed-Surface Segmentation for Connectomics
TLDR
A practical cutting-plane approach to solve the MAP inference problem to global optimality despite its NP-hardness is proposed and this approach is applied to challenging large-scale 3D segmentation problems for neural circuit reconstruction (Connectomics), demonstrating the advantage of this higher-order model over independent decisions and finite-order approximations.
Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images
TLDR
The use of variation of information to measure segmentation accuracy is advocated, particularly in 3D electron microscopy images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.
Ilastik: Interactive learning and segmentation toolkit
Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as
Machines that learn to segment images: a crucial technology for connectomics
Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems
  • Fabian Tschopp
  • Computer Science
    2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
  • 2016
TLDR
This paper presents an extension to the Caffe library to increase throughput by predicting many pixels at once on a sliding window network successfully used for membrane classification, and shows that the method achieves a speedup of up to 57×, maintaining identical prediction results.
TED: A Tolerant Edit Distance for segmentation evaluation.
...
1
2
3
...