Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction

Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have disc...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2017-07, Vol.36 (7), p.1533-1541
Main Authors: Li, Rongjian, Zeng, Tao, Peng, Hanchuan, Ji, Shuiwang
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Artificial neural networks
BigNeuron
Brain
Convolution
Deep learning
Digital imaging
Identification methods
image denoising
Image processing
Image reconstruction
Image segmentation
Imaging, Three-Dimensional
Machine Learning
Microscopy
Morphology
Neural networks
Neural Networks (Computer)
neuron reconstruction
Neurons
Optical microscopy
Three dimensional models
Three-dimensional displays
Wiring
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Summary:Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2017.2679713