Mr. Lijun Shen Profile

Lijun Shen

at Institute of Automation

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Proceedings Article | 18 March 2019 Presentation + Paper

Skeleton-based image registration of serial electron microscopy sections

Xi Chen, Lijun Shen, Qiwei Xie, Hua Han

Proceedings Volume 10956, 1095605 (2019) https://doi.org/10.1117/12.2512808

KEYWORDS: Image registration, Tissues, Electron microscopy

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Imaging serial sections in electron microcopy (EM) is an important volume EM approach for neuronal circuit reconstruction, which has advantages of larger imaging volume and non-destructive for tissue sections. However, the continuity between sections is destroyed when the tissue block is cut into sections physically, and sections suffer stretching, folding and distorting individually during section preparation and imaging. As a result, image registration is a challenging task to recover the continuity of the neurite. The traditional methods use the SIFT or block matching method to extract landmarks between the adjacent sections, which is doubtful when the neurite direction is not perpendicular to the section plane. To get round the difficulty of reliable landmark extraction, we propose a skeleton-based image registration method for serial EM sections of the nerve tissue. The virtual skeletons are traced across the sections after an initial approximate rigid alignment. Then we make assumption that the skeleton shape is smooth adequately in z direction. In company with the constraints that the displacements of the skeleton points in the same section are smooth and small, an energy function is proposed to calculate the new positions of the skeleton points for all of the sections. Finally, the sections are warped according to the adjusted positions of skeleton points. The proposed method is highly automatic and could recover the 3D continuity of the neurite. We demonstrate that our method outperforms the state-of-the-art methods on serial EM sections including a synthetic test case.

Proceedings Article | 24 February 2017 Paper

Deep learning and shapes similarity for joint segmentation and tracing single neurons in SEM images

Qiang Rao, Chi Xiao, Hua Han, Xi Chen, Lijun Shen, Qiwei Xie

Proceedings Volume 10133, 1013329 (2017) https://doi.org/10.1117/12.2254284

KEYWORDS: Convolutional neural networks, Electron microscopy, Neurons, Image segmentation, Scanning electron microscopy, Neural networks, Standards development, Machine learning, Brain, Tissues, Microscopy, Neuroimaging, Brain mapping

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Extracting the structure of single neurons is critical for understanding how they function within the neural circuits. Recent developments in microscopy techniques, and the widely recognized need for openness and standardization provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. In order to look into the fine structure of neurons, we use the Automated Tape-collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) to get images sequence of serial sections of animal brain tissue that densely packed with neurons. Different from other neuron reconstruction method, we propose a method that enhances the SEM images by detecting the neuronal membranes with deep convolutional neural network (DCNN) and segments single neurons by active contour with group shape similarity. We joint the segmentation and tracing together and they interact with each other by alternate iteration that tracing aids the selection of candidate region patch for active contour segmentation while the segmentation provides the neuron geometrical features which improve the robustness of tracing. The tracing model mainly relies on the neuron geometrical features and is updated after neuron being segmented on the every next section. Our method enables the reconstruction of neurons of the drosophila mushroom body which is cut to serial sections and imaged under SEM. Our method provides an elementary step for the whole reconstruction of neuronal networks.

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