Segmentation Enables Automated Registration for CLEM

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Correlative Microscopy Across the Scales
John Bogovic (1)
1. HHMI, Janelia Research Campus
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Automatic registration of light and electron microscopic images poses a difficult challenge due to resolution and contrast differences between the modalities, and the potentially non-linear transformations induced during sample preparation. We manually labeled 35 classes of cellular substructures across a variety of cell types to train deep neural networks for automatic organelle segmentation. This both enables structural / biological analyses and makes possible automated image registration. We generate synthetic images derived from the EM segmentation that resemble light images of fluorescent organelle markers, suitable for automatic pixel-based deformable registration algorithms.

We develop open source software tools and algorithms to enable high throughput analyses. We contribute to community efforts to create scalable and flexible computational infrastructure for these challenging tasks. These efforts include: ImageJ/Fiji, Imglib2, Bigdataviewer, BigWarp, N5, Paintera, and Elastix.