A modular hierarchical approach to 3D electron microscopy image segmentation

Ting Liu, Cory Jones, Mojtaba Seyedhosseini, Tolga Tasdizen
2014 Journal of Neuroscience Methods  
h i g h l i g h t s • Proposed a novel automatic hierarchical method for 2D EM image segmentation. • Proposed a novel semi-automatic method for 2D EM image segmentation with minimal user intervention. • Proposed a 3D linking method for 3D neuron reconstruction using 2D segmentations. • Achieved close-to-human 2D segmentation accuracy. • Achieved state-of-the-art 3D segmentation accuracy. a b s t r a c t The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in
more » ... euroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy.
doi:10.1016/j.jneumeth.2014.01.022 pmid:24491638 pmcid:PMC3970427 fatcat:3bm4j244infyzjgs4xjqlcv2um