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15 March 2019 Artificial neural network filters for enhancing 3D optical microscopy images of neurites
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Abstract
The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and neurological disorders. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (ANN) architectures and conventional image enhancement filters with the aim of alleviating both problems. We developed four image quality metrics to evaluate the effects of the proposed filters: normalized intensity in the cross-over regions between neurites, effective radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that ANN-based filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing the effective radius of neurites to nearly 1 voxel. In addition, this filter significantly decreases intensity in the cross-over regions between neurites and reduces fluctuations of intensity on neurites’ centerlines. These results suggest that including an ANN-based filtering step, which does not require substantial extra time or computing power, can be beneficial for automated neuron tracing projects.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shih-Luen Wang, Seyed M. M. Kahaki, and Armen Stepanyants "Artificial neural network filters for enhancing 3D optical microscopy images of neurites", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490G (15 March 2019); https://doi.org/10.1117/12.2512989
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